Radar apparatus, system, and method of generating angle of arrival (aoa) information

ABSTRACT

For example, a radar processor may be configured to determine a first 1D AoA spectrum corresponding to a first dimension of an Azimuth-Elevation domain based on radar Rx data, to determine a second 1D AoA spectrum corresponding to a second dimension of the Azimuth-Elevation domain based on the radar Rx data, to detect one or more first object hypotheses in the first dimension based on the first 1D AoA spectrum, to detect one or more second object hypotheses in the second dimension based on the second 1D AoA spectrum, to determine a plurality of 2D object hypotheses corresponding to the Azimuth-Elevation domain based on the first object hypotheses and the second object hypotheses, and to generate 2D AoA information based on a 2D AoA spectrum analysis of the radar Rx data according to the plurality of 2D object hypotheses.

TECHNICAL FIELD

Aspects described herein generally relate to radar devices, systems, andmethods to generate Angle of Arrival (AoA) information.

BACKGROUND

Various types of devices and systems, for example, autonomous and/orrobotic devices, e.g., autonomous vehicles and robots, may be configuredto perceive and navigate through their environment using sensor data ofone or more sensor types.

Conventionally, autonomous perception relies heavily on light-basedsensors, such as image sensors, e.g., cameras, and/or Light Detectionand Ranging (LIDAR) sensors. Such light-based sensors may perform poorlyunder certain conditions, such as, conditions of poor visibility, or incertain inclement weather conditions, e.g., rain, snow, hail, or otherforms of precipitation, thereby limiting their usefulness orreliability.

BRIEF DESCRIPTION OF THE DRAWINGS

For simplicity and clarity of illustration, elements shown in thefigures have not necessarily been drawn to scale. For example, thedimensions of some of the elements may be exaggerated relative to otherelements for clarity of presentation. Furthermore, reference numeralsmay be repeated among the figures to indicate corresponding or analogouselements. The figures are listed below.

FIG. 1 is a schematic block diagram illustration of a vehicleimplementing a radar, in accordance with some demonstrative aspects.

FIG. 2 is a schematic block diagram illustration of a robot implementinga radar, in accordance with some demonstrative aspects.

FIG. 3 is a schematic block diagram illustration of a radar apparatus,in accordance with some demonstrative aspects.

FIG. 4 is a schematic block diagram illustration of aFrequency-Modulated Continuous Wave (FMCW) radar apparatus, inaccordance with some demonstrative aspects.

FIG. 5 is a schematic illustration of an extraction scheme, which may beimplemented to extract range and speed (Doppler) estimations fromdigital reception radar data values, in accordance with somedemonstrative aspects.

FIG. 6 is a schematic illustration of an angle-determination scheme,which may be implemented to determine Angle of Arrival (AoA) informationbased on an incoming radio signal received by a receive antenna array,in accordance with some demonstrative aspects.

FIG. 7 is a schematic illustration of a Multiple-Input-Multiple-Output(MIMO) radar antenna scheme, which may be implemented based on acombination of Transmit (Tx) and Receive (Rx) antennas, in accordancewith some demonstrative aspects.

FIG. 8 is a schematic block diagram illustration of a radar frontend anda radar processor, in accordance with some demonstrative aspects.

FIG. 9 is a schematic illustration of a method of generatingtwo-dimensional (2D) Angle of Arrival (AoA) information, in accordancewith some demonstrative aspects.

FIG. 10A is a schematic illustration of a first one-dimensional (1D) AoAspectrum, and FIG. 10B is a schematic illustration of a second one 1DAoA spectrum, in accordance with some demonstrative aspects.

FIG. 11 is a schematic-flow chart illustration of a method of generating2D AoA information, in accordance with some demonstrative aspects.

FIG. 12 is a schematic illustration of a product of manufacture, inaccordance with some demonstrative aspects.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of some aspects.However, it will be understood by persons of ordinary skill in the artthat some aspects may be practiced without these specific details. Inother instances, well-known methods, procedures, components, unitsand/or circuits have not been described in detail so as not to obscurethe discussion.

Discussions herein utilizing terms such as, for example, “processing”,“computing”, “calculating”, “determining”, “establishing”, “analyzing”,“checking”, or the like, may refer to operation(s) and/or process(es) ofa computer, a computing platform, a computing system, or otherelectronic computing device, that manipulate and/or transform datarepresented as physical (e.g., electronic) quantities within thecomputer's registers and/or memories into other data similarlyrepresented as physical quantities within the computer's registersand/or memories or other information storage medium that may storeinstructions to perform operations and/or processes.

The terms “plurality” and “a plurality”, as used herein, include, forexample, “multiple” or “two or more”. For example, “a plurality ofitems” includes two or more items.

The words “exemplary” and “demonstrative” are used herein to mean“serving as an example, instance, demonstration, or illustration”. Anyaspect, embodiment, or design described herein as “exemplary” or“demonstrative” is not necessarily to be construed as preferred oradvantageous over other aspects, embodiments, or designs.

References to “one embodiment”, “an embodiment”, “demonstrativeembodiment”, “various embodiments” “one aspect”, “an aspect”,“demonstrative aspect”, “various aspects” etc., indicate that theembodiment(s) and/or aspects so described may include a particularfeature, structure, or characteristic, but not every embodiment oraspect necessarily includes the particular feature, structure, orcharacteristic. Further, repeated use of the phrase “in one embodiment”or “in one aspect” does not necessarily refer to the same embodiment oraspect, although it may.

As used herein, unless otherwise specified the use of the ordinaladjectives “first”, “second”, “third” etc., to describe a common object,merely indicate that different instances of like objects are beingreferred to, and are not intended to imply that the objects so describedmust be in a given sequence, either temporally, spatially, in ranking,or in any other manner.

The phrases “at least one” and “one or more” may be understood toinclude a numerical quantity greater than or equal to one, e.g., one,two, three, four, [ . . . ], etc. The phrase “at least one of” withregard to a group of elements may be used herein to mean at least oneelement from the group consisting of the elements. For example, thephrase “at least one of” with regard to a group of elements may be usedherein to mean one of the listed elements, a plurality of one of thelisted elements, a plurality of individual listed elements, or aplurality of a multiple of individual listed elements.

The term “data” as used herein may be understood to include informationin any suitable analog or digital form, e.g., provided as a file, aportion of a file, a set of files, a signal or stream, a portion of asignal or stream, a set of signals or streams, and the like. Further,the term “data” may also be used to mean a reference to information,e.g., in form of a pointer. The term “data”, however, is not limited tothe aforementioned examples and may take various forms and/or mayrepresent any information as understood in the art.

The terms “processor” or “controller” may be understood to include anykind of technological entity that allows handling of any suitable typeof data and/or information. The data and/or information may be handledaccording to one or more specific functions executed by the processor orcontroller. Further, a processor or a controller may be understood asany kind of circuit, e.g., any kind of analog or digital circuit. Aprocessor or a controller may thus be or include an analog circuit,digital circuit, mixed-signal circuit, logic circuit, processor,microprocessor, Central Processing Unit (CPU), Graphics Processing Unit(GPU), Digital Signal Processor (DSP), Field Programmable Gate Array(FPGA), integrated circuit, Application Specific Integrated Circuit(ASIC), and the like, or any combination thereof. Any other kind ofimplementation of the respective functions, which will be describedbelow in further detail, may also be understood as a processor,controller, or logic circuit. It is understood that any two (or more)processors, controllers, or logic circuits detailed herein may berealized as a single entity with equivalent functionality or the like,and conversely that any single processor, controller, or logic circuitdetailed herein may be realized as two (or more) separate entities withequivalent functionality or the like.

The term “memory” is understood as a computer-readable medium (e.g., anon-transitory computer-readable medium) in which data or informationcan be stored for retrieval. References to “memory” may thus beunderstood as referring to volatile or non-volatile memory, includingrandom access memory (RAM), read-only memory (ROM), flash memory,solid-state storage, magnetic tape, hard disk drive, optical drive,among others, or any combination thereof. Registers, shift registers,processor registers, data buffers, among others, are also embracedherein by the term memory. The term “software” may be used to refer toany type of executable instruction and/or logic, including firmware.

A “vehicle” may be understood to include any type of driven object. Byway of example, a vehicle may be a driven object with a combustionengine, an electric engine, a reaction engine, an electrically drivenobject, a hybrid driven object, or a combination thereof. A vehicle maybe, or may include, an automobile, a bus, a mini bus, a van, a truck, amobile home, a vehicle trailer, a motorcycle, a bicycle, a tricycle, atrain locomotive, a train wagon, a moving robot, a personal transporter,a boat, a ship, a submersible, a submarine, a drone, an aircraft, arocket, among others.

A “ground vehicle” may be understood to include any type of vehicle,which is configured to traverse the ground, e.g., on a street, on aroad, on a track, on one or more rails, off-road, or the like.

An “autonomous vehicle” may describe a vehicle capable of implementingat least one navigational change without driver input. A navigationalchange may describe or include a change in one or more of steering,braking, acceleration/deceleration, or any other operation relating tomovement, of the vehicle. A vehicle may be described as autonomous evenin case the vehicle is not fully autonomous, for example, fullyoperational with driver or without driver input. Autonomous vehicles mayinclude those vehicles that can operate under driver control duringcertain time periods, and without driver control during other timeperiods. Additionally or alternatively, autonomous vehicles may includevehicles that control only some aspects of vehicle navigation, such assteering, e.g., to maintain a vehicle course between vehicle laneconstraints, or some steering operations under certain circumstances,e.g., not under all circumstances, but may leave other aspects ofvehicle navigation to the driver, e.g., braking or braking under certaincircumstances. Additionally or alternatively, autonomous vehicles mayinclude vehicles that share the control of one or more aspects ofvehicle navigation under certain circumstances, e.g., hands-on, such asresponsive to a driver input; and/or vehicles that control one or moreaspects of vehicle navigation under certain circumstances, e.g.,hands-off, such as independent of driver input. Additionally oralternatively, autonomous vehicles may include vehicles that control oneor more aspects of vehicle navigation under certain circumstances, suchas under certain environmental conditions, e.g., spatial areas, roadwayconditions, or the like. In some aspects, autonomous vehicles may handlesome or all aspects of braking, speed control, velocity control,steering, and/or any other additional operations, of the vehicle. Anautonomous vehicle may include those vehicles that can operate without adriver. The level of autonomy of a vehicle may be described ordetermined by the Society of Automotive Engineers (SAE) level of thevehicle, e.g., as defined by the SAE, for example in SAE J3016 2018:Taxonomy and definitions for terms related to driving automation systemsfor on road motor vehicles, or by other relevant professionalorganizations. The SAE level may have a value ranging from a minimumlevel, e.g., level 0 (illustratively, substantially no drivingautomation), to a maximum level, e.g., level 5 (illustratively, fulldriving automation).

The phrase “vehicle operation data” may be understood to describe anytype of feature related to the operation of a vehicle. By way ofexample, “vehicle operation data” may describe the status of thevehicle, such as, the type of tires of the vehicle, the type of vehicle,and/or the age of the manufacturing of the vehicle. More generally,“vehicle operation data” may describe or include static features orstatic vehicle operation data (illustratively, features or data notchanging over time). As another example, additionally or alternatively,“vehicle operation data” may describe or include features changingduring the operation of the vehicle, for example, environmentalconditions, such as weather conditions or road conditions during theoperation of the vehicle, fuel levels, fluid levels, operationalparameters of the driving source of the vehicle, or the like. Moregenerally, “vehicle operation data” may describe or include varyingfeatures or varying vehicle operation data (illustratively, time varyingfeatures or data).

Some aspects may be used in conjunction with various devices andsystems, for example, a radar sensor, a radar device, a radar system, avehicle, a vehicular system, an autonomous vehicular system, a vehicularcommunication system, a vehicular device, an airborne platform, awaterborne platform, road infrastructure, sports-capture infrastructure,city monitoring infrastructure, static infrastructure platforms, indoorplatforms, moving platforms, robot platforms, industrial platforms, asensor device, a User Equipment (UE), a Mobile Device (MD), a wirelessstation (STA), a sensor device, a non-vehicular device, a mobile orportable device, and the like.

Some aspects may be used in conjunction with Radio Frequency (RF)systems, radar systems, vehicular radar systems, autonomous systems,robotic systems, detection systems, or the like.

Some demonstrative aspects may be used in conjunction with an RFfrequency in a frequency band having a starting frequency above 10Gigahertz (GHz), for example, a frequency band having a startingfrequency between 10 GHz and 120 GHz. For example, some demonstrativeaspects may be used in conjunction with an RF frequency having astarting frequency above 30 GHz, for example, above 45 GHz, e.g., above60 GHz. For example, some demonstrative aspects may be used inconjunction with an automotive radar frequency band, e.g., a frequencyband between 76 GHz and 81 GHz. However, other aspects may beimplemented utilizing any other suitable frequency bands, for example, afrequency band above 140 GHz, a frequency band of 300 GHz, a subTerahertz (THz) band, a THz band, an Infra Red (IR) band, and/or anyother frequency band.

As used herein, the term “circuitry” may refer to, be part of, orinclude, an Application Specific Integrated Circuit (ASIC), anintegrated circuit, an electronic circuit, a processor (shared,dedicated, or group), and/or memory (shared, dedicated, or group), thatexecute one or more software or firmware programs, a combinational logiccircuit, and/or other suitable hardware components that provide thedescribed functionality. In some aspects, the circuitry may beimplemented in, or functions associated with the circuitry may beimplemented by, one or more software or firmware modules. In someaspects, circuitry may include logic, at least partially operable inhardware.

The term “logic” may refer, for example, to computing logic embedded incircuitry of a computing apparatus and/or computing logic stored in amemory of a computing apparatus. For example, the logic may beaccessible by a processor of the computing apparatus to execute thecomputing logic to perform computing functions and/or operations. In oneexample, logic may be embedded in various types of memory and/orfirmware, e.g., silicon blocks of various chips and/or processors. Logicmay be included in, and/or implemented as part of, various circuitry,e.g., radio circuitry, receiver circuitry, control circuitry,transmitter circuitry, transceiver circuitry, processor circuitry,and/or the like. In one example, logic may be embedded in volatilememory and/or non-volatile memory, including random access memory, readonly memory, programmable memory, magnetic memory, flash memory,persistent memory, and/or the like. Logic may be executed by one or moreprocessors using memory, e.g., registers, buffers, stacks, and the like,coupled to the one or more processors, e.g., as necessary to execute thelogic.

The term “communicating” as used herein with respect to a signalincludes transmitting the signal and/or receiving the signal. Forexample, an apparatus, which is capable of communicating a signal, mayinclude a transmitter to transmit the signal, and/or a receiver toreceive the signal. The verb communicating may be used to refer to theaction of transmitting or the action of receiving. In one example, thephrase “communicating a signal” may refer to the action of transmittingthe signal by a transmitter, and may not necessarily include the actionof receiving the signal by a receiver. In another example, the phrase“communicating a signal” may refer to the action of receiving the signalby a receiver, and may not necessarily include the action oftransmitting the signal by a transmitter.

The term “antenna”, as used herein, may include any suitableconfiguration, structure and/or arrangement of one or more antennaelements, components, units, assemblies and/or arrays. In some aspects,the antenna may implement transmit and receive functionalities usingseparate transmit and receive antenna elements. In some aspects, theantenna may implement transmit and receive functionalities using commonand/or integrated transmit/receive elements. The antenna may include,for example, a phased array antenna, a single element antenna, a set ofswitched beam antennas, and/or the like. In one example, an antenna maybe implemented as a separate element or an integrated element, forexample, as an on-module antenna, an on-chip antenna, or according toany other antenna architecture.

Some demonstrative aspects are described herein with respect to RF radarsignals. However, other aspects may be implemented with respect to, orin conjunction with, any other radar signals, wireless signals, IRsignals, acoustic signals, optical signals, wireless communicationsignals, communication scheme, network, standard, and/or protocol. Forexample, some demonstrative aspects may be implemented with respect tosystems, e.g., Light Detection Ranging (LiDAR) systems, and/or sonarsystems, utilizing light and/or acoustic signals.

Reference is now made to FIG. 1, which schematically illustrates a blockdiagram of a vehicle 100 implementing a radar, in accordance with somedemonstrative aspects.

In some demonstrative aspects, vehicle 100 may include a car, a truck, amotorcycle, a bus, a train, an airborne vehicle, a waterborne vehicle, acart, a golf cart, an electric cart, a road agent, or any other vehicle.

In some demonstrative aspects, vehicle 100 may include a radar device101, e.g., as described below. For example, radar device 101 may includea radar detecting device, a radar sensing device, a radar sensor, or thelike, e.g., as described below.

In some demonstrative aspects, radar device 101 may be implemented aspart of a vehicular system, for example, a system to be implementedand/or mounted in vehicle 100.

In one example, radar device 101 may be implemented as part of anautonomous vehicle system, an automated driving system, a driverassistance and/or support system, and/or the like.

For example, radar device 101 may be installed in vehicle 101 fordetection of nearby objects, e.g., for autonomous driving.

In some demonstrative aspects, radar device 101 may be configured todetect targets in a vicinity of vehicle 100, e.g., in a far vicinityand/or a near vicinity, for example, using RF and analog chains,capacitor structures, large spiral transformers and/or any otherelectronic or electrical elements, e.g., as described below. In oneexample, radar device 101 may be mounted onto, placed, e.g., directly,onto, or attached to, vehicle 100.

In some demonstrative aspects, vehicle 100 may include a single radardevice 101. In other aspects, vehicle 100 may include a plurality ofradar devices 101, for example, at a plurality of locations, e.g.,around vehicle 100.

In some demonstrative aspects, radar device 101 may be implemented as acomponent in a suite of sensors used for driver assistance and/orautonomous vehicles, for example, due to the ability of radar to operatein nearly all-weather conditions.

In some demonstrative aspects, radar device 101 may be configured tosupport autonomous vehicle usage, e.g., as described below.

In one example, radar device 101 may determine a class, a location, anorientation, a velocity, an intention, a perceptional understanding ofthe environment, and/or any other information corresponding to an objectin the environment.

In another example, radar device 101 may be configured to determine oneor more parameters and/or information for one or more operations and/ortasks, e.g., path planning, and/or any other tasks.

In some demonstrative aspects, radar device 101 may be configured to mapa scene by measuring targets' echoes (reflectivity) and discriminatingthem, for example, mainly in range, velocity, azimuth and/or elevation,e.g., as described below.

In some demonstrative aspects, radar device 101 may be configured todetect, and/or sense, one or more objects, which are located in avicinity, e.g., a far vicinity and/or a near vicinity, of the vehicle100, and to provide one or more parameters, attributes, and/orinformation with respect to the objects.

In some demonstrative aspects, the objects may include other vehicles;pedestrians; traffic signs; traffic lights; roads, road elements, e.g.,a pavement-road meeting, an edge line; a hazard, e.g., a tire, a box, acrack in the road surface; and/or the like.

In some demonstrative aspects, the one or more parameters, attributesand/or information with respect to the object may include a range of theobjects from the vehicle 100, an angle of the object with respect to thevehicle 100, a location of the object with respect to the vehicle 100, arelative speed of the object with respect to vehicle 100, and/or thelike.

In some demonstrative aspects, radar device 101 may include a MultipleInput Multiple Output (MIMO) radar device 101, e.g., as described below.In one example, the MIMO radar device may be configured to utilize“spatial filtering” processing, for example, beamforming and/or anyother mechanism, for one or both of Transmit (Tx) signals and/or Receive(Rx) signals.

Some demonstrative aspects are described below with respect to a radardevice, e.g., radar device 101, implemented as a MIMO radar. However, inother aspects, radar device 101 may be implemented as any other type ofradar utilizing a plurality of antenna elements, e.g., a Single InputMultiple Output (SIMO) radar or a Multiple Input Single output (MISO)radar.

Some demonstrative aspects may be implemented with respect to a radardevice, e.g., radar device 101, implemented as a MIMO radar, e.g., asdescribed below. However, in other aspects, radar device 101 may beimplemented as any other type of radar, for example, an Electronic BeamSteering radar, a Synthetic Aperture Radar (SAR), adaptive and/orcognitive radars that change their transmission according to theenvironment and/or ego state, a reflect array radar, or the like.

In some demonstrative aspects, radar device 101 may include an antennaarrangement 102, a radar frontend 103 configured to communicate radarsignals via the antenna arrangement 102, and a radar processor 104configured to generate radar information based on the radar signals,e.g., as described below.

In some demonstrative aspects, radar processor 104 may be configured toprocess radar information of radar device 101 and/or to control one ormore operations of radar device 101, e.g., as described below.

In some demonstrative aspects, radar processor 104 may include, or maybe implemented, partially or entirely, by circuitry and/or logic, e.g.,one or more processors including circuitry and/or logic, memorycircuitry and/or logic. Additionally or alternatively, one or morefunctionalities of radar processor 104 may be implemented by logic,which may be executed by a machine and/or one or more processors, e.g.,as described below.

In one example, radar processor 104 may include at least one memory,e.g., coupled to the one or more processors, which may be configured,for example, to store, e.g., at least temporarily, at least some of theinformation processed by the one or more processors and/or circuitry,and/or which may be configured to store logic to be utilized by theprocessors and/or circuitry.

In other aspects, radar processor 104 may be implemented by one or moreadditional or alternative elements of vehicle 100.

In some demonstrative aspects, radar frontend 103 may include, forexample, one or more (radar) transmitters, and a one or more (radar)receivers, e.g., as described below.

In some demonstrative aspects, antenna arrangement 102 may include aplurality of antennas to communicate the radar signals. For example,antenna arrangement 102 may include multiple transmit antennas in theform of a transmit antenna array, and multiple receive antennas in theform of a receive antenna array. In another example, antenna arrangement102 may include one or more antennas used both as transmit and receiveantennas. In the latter case, the radar frontend 103, for example, mayinclude a duplexer, e.g., a circuit to separate transmitted signals fromreceived signals.

In some demonstrative aspects, as shown in FIG. 1, the radar frontend103 and the antenna arrangement 102 may be controlled, e.g., by radarprocessor 104, to transmit a radio transmit signal 105.

In some demonstrative aspects, as shown in FIG. 1, the radio transmitsignal 105 may be reflected by an object 106, resulting in an echo 107.

In some demonstrative aspects, the radar device 101 may receive the echo107, e.g., via antenna arrangement 102 and radar frontend 103, and radarprocessor 104 may generate radar information, for example, bycalculating information about position, radial velocity (Doppler),and/or direction of the object 106, e.g., with respect to vehicle 100.

In some demonstrative aspects, radar processor 104 may be configured toprovide the radar information to a vehicle controller 108 of the vehicle100, e.g., for autonomous driving of the vehicle 100.

In some demonstrative aspects, at least part of the functionality ofradar processor 104 may be implemented as part of vehicle controller108. In other aspects, the functionality of radar processor 104 may beimplemented as part of any other element of radar device 101 and/orvehicle 100. In other aspects, radar processor 104 may be implemented,as a separate part of, or as part of any other element of radar device101 and/or vehicle 100.

In some demonstrative aspects, vehicle controller 108 may be configuredto control one or more functionalities, modes of operation, components,devices, systems and/or elements of vehicle 100.

In some demonstrative aspects, vehicle controller 108 may be configuredto control one or more vehicular systems of vehicle 100, e.g., asdescribed below.

In some demonstrative aspects, the vehicular systems may include, forexample, a steering system, a braking system, a driving system, and/orany other system of the vehicle 100.

In some demonstrative aspects, vehicle controller 108 may configured tocontrol radar device 101, and/or to process one or parameters,attributes and/or information from radar device 101.

In some demonstrative aspects, vehicle controller 108 may be configured,for example, to control the vehicular systems of the vehicle 100, forexample, based on radar information from radar device 101 and/or one ormore other sensors of the vehicle 100, e.g., Light Detection and Ranging(LIDAR) sensors, camera sensors, and/or the like.

In one example, vehicle controller 108 may control the steering system,the braking system, and/or any other vehicular systems of vehicle 100,for example, based on the information from radar device 101, e.g., basedon one or more objects detected by radar device 101.

In other aspects, vehicle controller 108 may be configured to controlany other additional or alternative functionalities of vehicle 100.

Some demonstrative aspects are described herein with respect to a radardevice 101 implemented in a vehicle, e.g., vehicle 100. In other aspectsa radar device, e.g., radar device 101, may be implemented as part ofany other element of a traffic system or network, for example, as partof a road infrastructure, and/or any other element of a traffic networkor system. Other aspects may be implemented with respect to any othersystem, environment and/or apparatus, which may be implemented in anyother object, environment, location, or place. For example, radar device101 may be part of a non-vehicular device, which may be implemented, forexample, in an indoor location, a stationary infrastructure outdoors, orany other location.

In some demonstrative aspects, radar device 101 may be configured tosupport security usage. In one example, radar device 101 may beconfigured to determine a nature of an operation, e.g., a human entry,an animal entry, an environmental movement, and the like, to identity athreat level of a detected event, and/or any other additional oralternative operations.

Some demonstrative aspects may be implemented with respect to any otheradditional or alternative devices and/or systems, for example, for arobot, e.g., as described below.

In other aspects, radar device 101 may be configured to support anyother usages and/or applications.

Reference is now made to FIG. 2, which schematically illustrates a blockdiagram of a robot 200 implementing a radar, in accordance with somedemonstrative aspects.

In some demonstrative aspects, robot 200 may include a robot arm 201.The robot 200 may be implemented, for example, in a factory for handlingan object 213, which may be, for example, a part that should be affixedto a product that is being manufactured. The robot arm 201 may include aplurality of movable members, for example, movable members 202, 203,204, and a support 205. Moving the movable members 202, 203, and/or 204of the robot arm 201, e.g., by actuation of associated motors, may allowphysical interaction with the environment to carry out a task, e.g.,handling the object 213.

In some demonstrative aspects, the robot arm 201 may include a pluralityof joint elements, e.g., joint elements 207, 208, 209, which mayconnect, for example, the members 202, 203, and/or 204 with each other,and with the support 205. For example, a joint element 207, 208, 209 mayhave one or more joints, each of which may provide rotatable motion,e.g., rotational motion, and/or translatory motion, e.g., displacement,to associated members and/or motion of members relative to each other.The movement of the members 202, 203, 204 may be initiated by suitableactuators.

In some demonstrative aspects, the member furthest from the support 205,e.g., member 204, may also be referred to as the end-effector 204 andmay include one or more tools, such as, a claw for gripping an object, awelding tool, or the like. Other members, e.g., members 202, 203, closerto the support 205, may be utilized to change the position of theend-effector 204, e.g., in three-dimensional space. For example, therobot arm 201 may be configured to function similarly to a human arm,e.g., possibly with a tool at its end.

In some demonstrative aspects, robot 200 may include a (robot)controller 206 configured to implement interaction with the environment,e.g., by controlling the robot arm's actuators, according to a controlprogram, for example, in order to control the robot arm 201 according tothe task to be performed.

In some demonstrative aspects, an actuator may include a componentadapted to affect a mechanism or process in response to being driven.The actuator can respond to commands given by the controller 206 (theso-called activation) by performing mechanical movement. This means thatan actuator, typically a motor (or electromechanical converter), may beconfigured to convert electrical energy into mechanical energy when itis activated (i.e. actuated).

In some demonstrative aspects, controller 206 may be in communicationwith a radar processor 210 of the robot 200.

In some demonstrative aspects, a radar fronted 211 and a radar antennaarrangement 212 may be coupled to the radar processor 210. In oneexample, radar fronted 211 and/or radar antenna arrangement 212 may beincluded, for example, as part of the robot arm 201.

In some demonstrative aspects, the radar frontend 211, the radar antennaarrangement 212 and the radar processor 210 may be operable as, and/ormay be configured to form, a radar device. For example, antennaarrangement 212 may be configured to perform one or more functionalitiesof antenna arrangement 102 (FIG. 1), radar frontend 211 may beconfigured to perform one or more functionalities of radar frontend 103(FIG. 1), and/or radar processor 210 may be configured to perform one ormore functionalities of radar processor 104 (FIG. 1), e.g., as describedabove.

In some demonstrative aspects, for example, the radar frontend 211 andthe antenna arrangement 212 may be controlled, e.g., by radar processor210, to transmit a radio transmit signal 214.

In some demonstrative aspects, as shown in FIG. 2, the radio transmitsignal 214 may be reflected by the object 213, resulting in an echo 215.

In some demonstrative aspects, the echo 215 may be received, e.g., viaantenna arrangement 212 and radar frontend 211, and radar processor 210may generate radar information, for example, by calculating informationabout position, speed (Doppler) and/or direction of the object 213,e.g., with respect to robot arm 201.

In some demonstrative aspects, radar processor 210 may be configured toprovide the radar information to the robot controller 206 of the robotarm 201, e.g., to control robot arm 201. For example, robot controller206 may be configured to control robot arm 201 based on the radarinformation, e.g., to grab the object 213 and/or to perform any otheroperation.

Reference is made to FIG. 3, which schematically illustrates a radarapparatus 300, in accordance with some demonstrative aspects.

In some demonstrative aspects, radar apparatus 300 may be implemented aspart of a device or system 301, e.g., as described below.

For example, radar apparatus 300 may be implemented as part of, and/ormay configured to perform one or more operations and/or functionalitiesof, the devices or systems described above with reference to FIG. 1an/or FIG. 2. In other aspects, radar apparatus 300 may be implementedas part of any other device or system 301.

In some demonstrative aspects, radar device 300 may include an antennaarrangement, which may include one or more transmit antennas 302 and oneor more receive antennas 303. In other aspects, any other antennaarrangement may be implemented.

In some demonstrative aspects, radar device 300 may include a radarfrontend 304, and a radar processor 309.

In some demonstrative aspects, as shown in FIG. 3, the one or moretransmit antennas 302 may be coupled with a transmitter (or transmitterarrangement) 305 of the radar frontend 304; and/or the one or morereceive antennas 303 may be coupled with a receiver (or receiverarrangement) 306 of the radar frontend 304, e.g., as described below.

In some demonstrative aspects, transmitter 305 may include one or moreelements, for example, an oscillator, a power amplifier and/or one ormore other elements, configured to generate radio transmit signals to betransmitted by the one or more transmit antennas 302, e.g., as describedbelow.

In some demonstrative aspects, for example, radar processor 309 mayprovide digital radar transmit data values to the radar frontend 304.For example, radar frontend 304 may include a Digital-to-AnalogConverter (DAC) 307 to convert the digital radar transmit data values toan analog transmit signal. The transmitter 305 may convert the analogtransmit signal to a radio transmit signal which is to be transmitted bytransmit antennas 302.

In some demonstrative aspects, receiver 306 may include one or moreelements, for example, one or more mixers, one or more filters and/orone or more other elements, configured to process, down-convert, radiosignals received via the one or more receive antennas 303, e.g., asdescribed below.

In some demonstrative aspects, for example, receiver 306 may convert aradio receive signal received via the one or more receive antennas 303into an analog receive signal. The radar frontend 304 may include anAnalog-to-Digital (ADC) Converter 308 to generate digital radarreception data values based on the analog receive signal. For example,radar frontend 304 may provide the digital radar reception data valuesto the radar processor 309.

In some demonstrative aspects, radar processor 309 may be configured toprocess the digital radar reception data values, for example, to detectone or more objects, e.g., in an environment of the device/system 301.This detection may include, for example, the determination ofinformation including one or more of range, speed (Doppler), direction,and/or any other information, of one or more objects, e.g., with respectto the system 301.

In some demonstrative aspects, radar processor 309 may be configured toprovide the determined radar information to a system controller 310 ofdevice/system 301. For example, system controller 310 may include avehicle controller, e.g., if device/system 301 includes a vehiculardevice/system, a robot controller, e.g., if device/system 301 includes arobot device/system, or any other type of controller for any other typeof device/system 301.

In some demonstrative aspects, system controller 310 may be configuredto control one or more controlled system components 311 of the system301, e.g. a motor, a brake, steering, and the like, e.g. by one or morecorresponding actuators.

In some demonstrative aspects, radar device 300 may include a storage312 or a memory 313, e.g., to store information processed by radar 300,for example, digital radar reception data values being processed by theradar processor 309, radar information generated by radar processor 309,and/or any other data to be processed by radar processor 309.

In some demonstrative aspects, device/system 301 may include, forexample, an application processor 314 and/or a communication processor315, for example, to at least partially implement one or morefunctionalities of system controller 310 and/or to perform communicationbetween system controller 310, radar device 300, the controlled systemcomponents 311, and/or one or more additional elements of device/system301.

In some demonstrative aspects, radar device 300 may be configured togenerate and transmit the radio transmit signal in a form, which maysupport determination of range, speed, and/or direction, e.g., asdescribed below.

For example, a radio transmit signal of a radar may be configured toinclude a plurality of pulses. For example, a pulse transmission mayinclude the transmission of short high-power bursts in combination withtimes during which the radar device listens for echoes.

For example, in order to more optimally support a highly dynamicsituation, e.g., in an automotive scenario, a continuous wave (CW) mayinstead be used as the radio transmit signal. However, a continuouswave, e.g., with constant frequency, may support velocity determination,but may not allow range determination, e.g., due to the lack of a timemark that could allow distance calculation.

In some demonstrative aspects, radio transmit signal 105 (FIG. 1) may betransmitted according to technologies such as, for example,Frequency-Modulated continuous wave (FMCW) radar, Phase-ModulatedContinuous Wave (PMCW) radar, Orthogonal Frequency Division Multiplexing(OFDM) radar, and/or any other type of radar technology, which maysupport determination of range, velocity, and/or direction, e.g., asdescribed below.

Reference is made to FIG. 4, which schematically illustrates a FMCWradar apparatus, in accordance with some demonstrative aspects.

In some demonstrative aspects, FMCW radar device 400 may include a radarfrontend 401, and a radar processor 402. For example, radar frontend 304(FIG. 3) may include one or more elements of, and/or may perform one ormore operations and/or functionalities of, radar frontend 401; and/orradar processor 309 (FIG. 3) may include one or more elements of, and/ormay perform one or more operations and/or functionalities of, radarprocessor 402.

In some demonstrative aspects, FMCW radar device 400 may be configuredto communicate radio signals according to an FMCW radar technology,e.g., rather than sending a radio transmit signal with a constantfrequency.

In some demonstrative aspects, radio frontend 401 may be configured toramp up and reset the frequency of the transmit signal, e.g.,periodically, for example, according to a saw tooth waveform 403. Inother aspects, a triangle waveform, or any other suitable waveform maybe used.

In some demonstrative aspects, for example, radar processor 402 may beconfigured to provide waveform 403 to frontend 401, for example, indigital form, e.g., as a sequence of digital values.

In some demonstrative aspects, radar frontend 401 may include a DAC 404to convert waveform 403 into analog form, and to supply it to avoltage-controlled oscillator 405. For example, oscillator 405 may beconfigured to generate an output signal, which may befrequency-modulated in accordance with the waveform 403.

In some demonstrative aspects, oscillator 405 may be configured togenerate the output signal including a radio transmit signal, which maybe fed to and sent out by one or more transmit antennas 406.

In some demonstrative aspects, the radio transmit signal generated bythe oscillator 405 may have the form of a sequence of chirps 407, whichmay be the result of the modulation of a sinusoid with the saw toothwaveform 403.

In one example, a chirp 407 may correspond to the sinusoid of theoscillator signal frequency-modulated by a “tooth” of the saw toothwaveform 403, e.g., from the minimum frequency to the maximum frequency.

In some demonstrative aspects, FMCW radar device 400 may include one ormore receive antennas 408 to receive a radio receive signal. The radioreceive signal may be based on the echo of the radio transmit signal,e.g., in addition to any noise, interference, or the like.

In some demonstrative aspects, radar frontend 401 may include a mixer409 to mix the radio transmit signal with the radio receive signal intoa mixed signal.

In some demonstrative aspects, radar frontend 401 may include a filter,e.g., a Low Pass Filter (LPF) 410, which may be configured to filter themixed signal from the mixer 409 to provide a filtered signal. Forexample, radar frontend 401 may include an ADC 411 to convert thefiltered signal into digital reception data values, which may beprovided to radar processor 402. In another example, the filter 410 maybe a digital filter, and the ADC 411 may be arranged between the mixer409 and the filter 410.

In some demonstrative aspects, radar processor 402 may be configured toprocess the digital reception data values to provide radar information,for example, including range, speed (velocity/Doppler), and/or direction(AoA) information of one or more objects.

In some demonstrative aspects, radar processor 402 may be configured toperform a first Fast Fourier Transform (FFT) (also referred to as “rangeFFT”) to extract a delay response, which may be used to extract rangeinformation, and/or a second FFT (also referred to as “Doppler FFT”) toextract a Doppler shift response, which may be used to extract velocityinformation, from the digital reception data values.

In other aspects, any other additional or alternative methods may beutilized to extract range information. In one example, in a digitalradar implementation, a correlation with the transmitted signal may beused, e.g., according to a matched filter implementation.

Reference is made to FIG. 5, which schematically illustrates anextraction scheme, which may be implemented to extract range and speed(Doppler) estimations from digital reception radar data values, inaccordance with some demonstrative aspects. For example, radar processor104 (FIG. 1), radar processor 210 (FIG. 2), radar processor 309 (FIG.3), and/or radar processor 402 (FIG. 4), may be configured to extractrange and/or speed (Doppler) estimations from digital reception radardata values according to one or more aspects of the extraction scheme ofFIG. 5.

In some demonstrative aspects, as shown in FIG. 5, a radio receivesignal, e.g., including echoes of a radio transmit signal, may bereceived by a receive antenna array 501. The radio receive signal may beprocessed by a radio radar frontend 502 to generate digital receptiondata values, e.g., as described above. The radio radar frontend 502 mayprovide the digital reception data values to a radar processor 503,which may process the digital reception data values to provide radarinformation, e.g., as described above.

In some demonstrative aspects, the digital reception data values may berepresented in the form of a data cube 504. For example, the data cube504 may include digitized samples of the radio receive signal, which isbased on a radio signal transmitted from a transmit antenna and receivedby M receive antennas. In some demonstrative aspects, for example, withrespect to a MIMO implementation, there may be multiple transmitantennas, and the number of samples may be multiplied accordingly.

In some demonstrative aspects, a layer of the data cube 504, forexample, a horizontal layer of the data cube 504, may include samples ofan antenna, e.g., a respective antenna of the M antennas.

In some demonstrative aspects, data cube 504 may include samples for Kchirps. For example, as shown in FIG. 5, the samples of the chirps maybe arranged in a so-called “slow time”-direction.

In some demonstrative aspects, the data cube 504 may include L samples,e.g., L=512 or any other number of samples, for a chirp, e.g., per eachchirp. For example, as shown in FIG. 5, the samples per chirp may bearranged in a so-called “fast time”-direction of the data cube 504.

In some demonstrative aspects, radar processor 503 may be configured toprocess a plurality of samples, e.g., L samples collected for each chirpand for each antenna, by a first FFT. The first FFT may be performed,for example, for each chirp and each antenna, such that a result of theprocessing of the data cube 504 by the first FFT may again have threedimensions, and may have the size of the data cube 504 while includingvalues for L range bins, e.g., instead of the values for the L samplingtimes.

In some demonstrative aspects, radar processor 503 may be configured toprocess the result of the processing of the data cube 504 by the firstFFT, for example, by processing the result according to a second FFTalong the chirps, e.g., for each antenna and for each range bin.

For example, the first FFT may be in the “fast time” direction, and thesecond FFT may be in the “slow time” direction.

In some demonstrative aspects, the result of the second FFT may provide,e.g., when aggregated over the antennas, a range/Doppler (R/D) map 505.The R/D map may have FFT peaks 506, for example, including peaks of FFToutput values (in terms of absolute values) for certain range/speedcombinations, e.g., for range/Doppler bins. For example, a range/Dopplerbin may correspond to a range bin and a Doppler bin. For example, radarprocessor 503 may consider a peak as potentially corresponding to anobject, e.g., of the range and speed corresponding to the peak's rangebin and speed bin.

In some demonstrative aspects, the extraction scheme of FIG. 5 may beimplemented for an FMCW radar, e.g., FMCW radar 400 (FIG. 4), asdescribed above. In other aspects, the extraction scheme of FIG. 5 maybe implemented for any other radar type. In one example, the radarprocessor 503 may be configured to determine a range/Doppler map 505from digital reception data values of a PMCW radar, an OFDM radar, orany other radar technologies. For example, in adaptive or cognitiveradar, the pulses in a frame, the waveform and/or modulation may bechanged over time, e.g., according to the environment.

Referring back to FIG. 3, in some demonstrative aspects, receive antennaarrangement 303 may be implemented using a receive antenna array havinga plurality of receive antennas (or receive antenna elements). Forexample, radar processor 309 may be configured to determine an angle ofarrival of the received radio signal, e.g., echo 105 (FIG. 1) and/orecho 215 (FIG. 2). For example, radar processor 309 may be configured todetermine a direction of a detected object, e.g., with respect to thedevice/system 301, for example, based on the angle of arrival of thereceived radio signal, e.g., as described below.

Reference is made to FIG. 6, which schematically illustrates anangle-determination scheme, which may be implemented to determine Angleof Arrival (AoA) information based on an incoming radio signal receivedby a receive antenna array 600, in accordance with some demonstrativeaspects.

FIG. 6 depicts an angle-determination scheme based on received signalsat the receive antenna array. In some demonstrative aspects, forexample, in a virtual MIMO array, the angle-determination may also bebased on the signals transmitted by the array of Tx antennas.

FIG. 6 depicts a one-dimensional angle-determination scheme. Othermulti-dimensional angle determination schemes, e.g., a two-dimensionalscheme or a three-dimensional scheme, may be implemented.

In some demonstrative aspects, as shown in FIG. 6, the receive antennaarray 600 may include M antennas (numbered, from left to right, 1 to M).

As shown by the arrows in FIG. 6, it is assumed that an echo is comingfrom an object located at the top left direction. Accordingly, thedirection of the echo, e.g., the incoming radio signal, may be towardsthe bottom right. According to this example, the further to the left areceive antenna is located, the earlier it will receive a certain phaseof the incoming radio signal.

For example, a phase difference, denoted Asp, between two antennas ofthe receive antenna array 601 may be determined, e.g., as follows:

${\Delta\phi} = {\frac{2\pi}{\lambda} \cdot d \cdot {\sin (\theta)}}$

wherein λ denotes a wavelength of the incoming radio signal, d denotes adistance between the two antennas, and θ denotes an angle of arrival ofthe incoming radio signal, e.g., with respect to a normal direction ofthe array.

In some demonstrative aspects, radar processor 309 (FIG. 3) may beconfigured to utilize this relationship between phase and angle of theincoming radio signal, for example, to determine the angle of arrival ofechoes, for example by performing an FFT, e.g., a third FFT (“angularFFT”) over the antennas.

In some demonstrative aspects, multiple transmit antennas, e.g., in theform of an antenna array having multiple transmit antennas, may be used,for example, to increase the spatial resolution, e.g., to providehigh-resolution radar information. For example, a MIMO radar device mayutilize a virtual MIMO radar antenna, which may be formed as aconvolution of a plurality of transmit antennas convolved with aplurality of receive antennas.

Reference is made to FIG. 7, which schematically illustrates a MIMOradar antenna scheme, which may be implemented based on a combination ofTransmit (Tx) and Receive (Rx) antennas, in accordance with somedemonstrative aspects.

In some demonstrative aspects, as shown in FIG. 7, a radar MIMOarrangement may include a transmit antenna array 701 and a receiveantenna array 702. For example, the one or more transmit antennas 302(FIG. 3) may be implemented to include transmit antenna array 701,and/or the one or more receive antennas 303 (FIG. 3) may be implementedto include receive antenna array 702.

In some demonstrative aspects, antenna arrays including multipleantennas both for transmitting the radio transmit signals and forreceiving echoes of the radio transmit signals, may be utilized toprovide a plurality of virtual channels as illustrated by the dashedlines in FIG. 7. For example, a virtual channel may be formed as aconvolution, for example, as a Kronecker product, between a transmitantenna and a receive antenna, e.g., representing a virtual steeringvector of the MIMO radar.

In some demonstrative aspects, a transmit antenna, e.g., each transmitantenna, may be configured to send out an individual radio transmitsignal, e.g., having a phase associated with the respective transmitantenna.

For example, an array of N transmit antennas and M receive antennas maybe implemented to provide a virtual MIMO array of size N×M. For example,the virtual MIMO array may be formed according to the Kronecker productoperation applied to the Tx and Rx steering vectors.

FIG. 8 is a schematic block diagram illustration of a radar frontend 804and a radar processor 834, in accordance with some demonstrativeaspects. For example, radar frontend 103 (FIG. 1), radar frontend 211(FIG. 1), radar frontend 304 (FIG. 3), radar frontend 401 (FIG. 4),and/or radar frontend 502 (FIG. 5), may include one or more elements ofradar frontend 804, and/or may perform one or more operations and/orfunctionalities of radar frontend 804.

In some demonstrative aspects, radar frontend 804 may be implemented aspart of a radar utilizing a radar antenna 881 including a plurality ofTx antennas 814 configured to transmit a plurality of Tx RF signals(also referred to as “Tx radar signals”); and a plurality of Rx antennas816 configured to receive a plurality of Rx RF signals (also referred toas “Rx radar signals”), for example, based on the Tx radar signals,e.g., as described below.

In some demonstrative aspects, radar antenna 881 may include a MIMOradar antenna 881 including the plurality of Tx antennas 814 and theplurality of Rx antennas 816.

In some demonstrative aspects, MIMO antenna array 881, antennas 814,and/or antennas 816 may include or may be part of any type of antennassuitable for transmitting and/or receiving radar signals. For example,MIMO antenna array 881, antennas 814, and/or antennas 816, may beimplemented as part of any suitable configuration, structure, and/orarrangement of one or more antenna elements, components, units,assemblies, and/or arrays. For example, MIMO antenna array 881, antennas814, and/or antennas 816, may be implemented as part of a phased arrayantenna, a multiple element antenna, a set of switched beam antennas,and/or the like. In some aspects, MIMO antenna array 881, antennas 814,and/or antennas 816, may be implemented to support transmit and receivefunctionalities using separate transmit and receive antenna elements. Insome aspects, MIMO antenna array 881, antennas 814, and/or antennas 816,may be implemented to support transmit and receive functionalities usingcommon and/or integrated transmit/receive elements.

In some demonstrative aspects, MIMO radar antenna 881 may include arectangular MIMO antenna array, and/or curved array, e.g., shaped to fita vehicle design. In other aspects, any other form, shape and/orarrangement of MIMO radar antenna 881 may be implemented.

In some demonstrative aspects, radar frontend 804 may include one ormore radios configured to generate and transmit the Tx RF signals via Txantennas 814; and/or to process the Rx RF signals received via Rxantennas 816, e.g., as described below.

In some demonstrative aspects, radar frontend 804 may include at leastone transmitter (Tx) 883 including circuitry and/or logic configured togenerate and/or transmit the Tx radar signals via Tx antennas 814.

In some demonstrative aspects, radar frontend 804 may include at leastone receiver (Rx) 885 including circuitry and/or logic to receive and/orprocess the Rx radar signals received via Rx antennas 816, for example,based on the Tx radar signals.

In some demonstrative aspects, transmitter 883, and/or receiver 885 mayinclude circuitry; logic; Radio Frequency (RF) elements, circuitryand/or logic; baseband elements, circuitry and/or logic; modulationelements, circuitry and/or logic; demodulation elements, circuitryand/or logic; amplifiers; analog to digital and/or digital to analogconverters; filters; and/or the like.

In some demonstrative aspects, transmitter 883 may include a pluralityof Tx chains 810 configured to generate and transmit the Tx RF signalsvia Tx antennas 814, e.g., respectively; and/or receiver 885 may includea plurality of Rx chains 812 configured to receive and process the Rx RFsignals received via the Rx antennas 816, e.g., respectively.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 813, for example, based on the radar signalscommunicated by MIMO radar antenna 881, e.g., as described below. Forexample, radar processor 104 (FIG. 1), radar processor 210 (FIG. 1),radar processor 309 (FIG. 3), radar processor 402 (FIG. 4), and/or radarprocessor 503 (FIG. 5), may include one or more elements of radarprocessor 834, and/or may perform one or more operations and/orfunctionalities of radar processor 834.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 813, for example, based on Radar Rx data 811received from the plurality of Rx chains 812. For example, radar Rx data811 may be based on the Rx RF signals received via the Rx antennas 816.

In some demonstrative aspects, radar processor 834 may include an input832 to receive the radar Rx data 811 from the plurality of Rx chains812.

In some demonstrative aspects, radar processor 834 may include at leastone processor 836, which may be configured, for example, to process theradar Rx data 811, and/or to perform one or more operations, methods,and/or algorithms.

In some demonstrative aspects, radar processor 834 may include at leastone memory 838, e.g., coupled to the processor 836. For example, memory838 may be configured to store data processed by radar processor 834.For example, memory 838 may store, e.g., at least temporarily, at leastsome of the information processed by the processor 836, and/or logic tobe utilized by the processor 836.

In some demonstrative aspects, memory 838 may be configured to store atleast part of the radar data, e.g., some of the radar Rx data or all ofthe radar Rx data, for example, for processing by processor 836, e.g.,as described below.

In some demonstrative aspects, memory 838 may be configured to storeprocessed data, which may be generated by processor 836, for example,during the process of generating the radar information 813, e.g., asdescribed below.

In some demonstrative aspects, memory 838 may be configured to storerange information and/or Doppler information, which maybe generated byprocessor 836, for example, based on the radar Rx data, e.g., asdescribed below. In one example, the range information and/or Dopplerinformation may be determined based on a Cross-Correlation (XCORR)operation, which may be applied to the radar RX data, e.g., as describedbelow. Any other additional or alternative operation, algorithm and/orprocedure may be utilized to generate the range information and/orDoppler information.

In some demonstrative aspects, memory 838 may be configured to store AoAinformation, which maybe generated by processor 836, for example, basedon the radar Rx data, the range information and/or Doppler information,e.g., as described below. In one example, the AoA information may bedetermined based on an AoA estimation algorithm, e.g., as describedbelow. Any other additional or alternative operation, algorithm and/orprocedure may be utilized to generate the AoA information.

In some demonstrative aspects, radar processor 834 may be configured togenerate the radar information 813 including one or more of rangeinformation, Doppler information, and/or AoA information, e.g., asdescribed below.

In some demonstrative aspects, the radar information 813 may includePoint Cloud 1 (PC1) information, for example, including raw point cloudestimations, e.g., Range, Radial Velocity, Azimuth and/or Elevation.

In some demonstrative aspects, the radar information 813 may includePoint Cloud 2 (PC2) information, which may be generated, for example,based on the PC1 information. For example, the PC2 information mayinclude clustering information, tracking information, e.g., tracking ofprobabilities and/or density functions, bounding box information,classification information, orientation information, and the like.

In some demonstrative aspects, radar processor 834 may be configured togenerate the radar information 813 in the form of four Dimensional (4D)image information, e.g., a cube, which may represent 4D informationcorresponding to one or more detected targets.

In some demonstrative aspects, the 4D image information may include, forexample, range values, e.g., based on the range information, velocityvalues, e.g., based on the Doppler information, azimuth values, e.g.,based on azimuth AoA information, elevation values, e.g., based onelevation AoA information, and/or any other values.

In some demonstrative aspects, radar processor 834 may be configured togenerate the radar information 813 in any other form, and/or includingany other additional or alternative information.

In some demonstrative aspects, radar processor 834 may be configured toprocess the signals communicated via MIMO radar antenna 881 as signalsof a virtual MIMO array formed by a convolution of the plurality of Rxantennas 816 and the plurality of Tx antennas 814.

In some demonstrative aspects, radar frontend 804 and/or radar processor834 may be configured to utilize MIMO techniques, for example, tosupport a reduced physical array aperture, e.g., an array size, and/orutilizing a reduced number of antenna elements. For example, radarfrontend 804 and/or radar processor 834 may be configured to transmitorthogonal signals via a Tx array including a plurality of N elements,e.g., Tx antennas 814, and processing received signals via an Rx arrayincluding a plurality of M elements, e.g., Rx antennas 816.

In some demonstrative aspects, utilizing the MIMO technique oftransmission of the orthogonal signals from the Tx array with N elementsand processing the received signals in the Rx array with M elements maybe equivalent, e.g., under a far field approximation, to a radarutilizing transmission from one antenna and reception with N*M antennas.For example, radar frontend 804 and/or radar processor 834 may beconfigured to utilize MIMO antenna array 881 as a virtual array havingan equivalent array size of N*M, which may define locations of virtualelements, for example, as a convolution of locations of physicalelements, e.g., the antennas 814 and/or 816.

In some demonstrative aspects, radar processor 834 may be configured toestimate a target position in a 4D space, which may be represented, forexample, by a range, an azimuth (Az), an elevation (El), and a Velocity(V), e.g., as described below.

In some demonstrative aspects, radar processor 834 may be configured toestimate the azimuth and/or the elevation of the target, for example,using a planar 2 Dimensional (2D) antenna. For example, MIMO radarantenna 881 may include a 2D planar antenna array of Rx antennas 816.

Some demonstrative aspects are described herein with respect to a radarprocessor, e.g., radar processor 834, configured to process informationof a 2D antenna, e.g., as described below. In other aspects, radarprocessor 834 may be configured to process information of a 3Dimensional (3D) antenna.

In some demonstrative aspects, radar processor 834 may be configured togenerate radar information 813 including a 2D AoA information over anAzimuth-Elevation domain. For example, the 2D AoA spectrum over theAzimuth-Elevation domain may be utilized to estimate an Azimuth and/oran Elevation of a target.

In some demonstrative aspects, radar processor 834 may be configured togenerate the 2D AoA spectrum information, for example, according to oneor more requirements, and/or criteria, which may support a highdetection probability of targets, a low false-alarm rate of detectedtargets, and/or improved resolution, e.g., in terms of azimuth and/orelevation, for example, in a broad variance of target distribution,Signal to Noise Ratio (SNR), and/or dynamic range.

In some demonstrative aspects, there may be a need to provide atechnical solution, for example, to generate the 2D AoA spectruminformation, for example, according to the one or more requirements,and/or criteria, for example, while obviating a high computationcomplexity, e.g., as described below.

In some demonstrative aspects, there may be one or more technicaldisadvantages, inefficiencies, and/or problems, for example, in some usecases, implementations and/or scenarios, for example, when using aBeamforming (BF) filter to generate the 2D AoA spectrum information.

For example, the BF filter may provide suitable result with decentcomputation complexity. However, in order to reduce side lobes, a windowmay be used on an input array, which may enlarge a beam width and maycause a reduction in the AoA resolution.

In some demonstrative aspects, there may be a need to address one ormore technical issues, for example, in some use cases, implementationsand/or scenarios, for example, when using super resolution algorithms togenerate the 2D AoA spectrum information, e.g., as described below.

In one example, some super resolution algorithms may be suitable forproviding high resolution, and a low side lobe level, for example, withrespect to a sparse array topology. However, these super resolutionalgorithms may require very high computation complexity and/orcomputation resources, for example, for matrix inversion, iterationsand/or filtering, when applied with respect to large antenna arrays.

In some demonstrative aspects, radar processor 834 may be configured togenerate 2D AoA information, for example, with a high detectionprobability of targets, a low false-alarm rate, and/or an increasedresolution, for example, while supporting a reduced computationcomplexity, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured toreceive radar Rx data 811, e.g., via input 832, based on radar signalsof a 2D radar antenna, e.g., radar antenna 881, e.g., as describedbelow.

In some demonstrative aspects, radar processor 836 may be configured togenerate radar information 813 including 2D AoA information in theAzimuth-Elevation domain, for example, based on radar Rx data 811, e.g.,as described below.

In some demonstrative aspect, radar processor 836 may be configured togenerate radar information 813 including 2D AoA informationcorresponding to a range-Doppler bin, e.g., as described below.

In some demonstrative aspects, radar processor 834 may be configured todetermine radar data corresponding to a plurality of range-Doppler bins,for example, based on the radar Rx data 811, e.g., as described below.

In some demonstrative aspects, the plurality of range-Doppler bins maybe configured based on a setting and/or implementation of a radar deviceimplementing radar processor 834, e.g., radar device 101 (FIG. 1).

In some demonstrative aspects, radar processor 834 may be configured todetermine the radar data corresponding to the range-Doppler bins, forexample, by applying to the radar Rx data 811 a Cross Correlation(XCORR) operation, a fast Fourier Transfer (FFT) operation, and/or anyother operation, function and/or algorithm.

In some demonstrative aspects, the radar data corresponding to therange-Doppler bins may include, for example, information of a radarframe, e.g., as described below. In other aspects, the radar datacorresponding to the range-Doppler bins may include any other typeand/or format of radar data, e.g., intermediate data, and/or processeddata, which may be based on the radar Rx data 811.

In some demonstrative aspects, the radar data corresponding to therange-Doppler bins may be in the form of a radar frame, which maycorrespond to a plurality of range values, a plurality of Dopplervalues, a plurality of Rx channels, and a plurality of Tx channels.

In some demonstrative aspects, the plurality of range values may includea plurality of range bins, which may be configured based on a settingand/or implementation of a radar device implementing radar processor834, e.g., radar device 101 (FIG. 1).

In some demonstrative aspects, the plurality of Doppler values mayinclude a plurality of Doppler bins, which may be configured based on asetting and/or implementation of the radar device implementing radarprocessor 834, e.g., radar device 101 (FIG. 1).

In some demonstrative aspects, the plurality of Rx channels maycorrespond to the plurality of Rx antennas 816 and/or Rx chains 831.

In some demonstrative aspects, the plurality of Tx channels maycorrespond to the plurality of Tx antennas 814 and/or Tx chains 810.

In some demonstrative aspects, a range-Doppler-bin may correspond to acombination of a range value of the plurality of range values and aDoppler value of the plurality of Doppler values. For example, therange-Doppler bin may include radar data corresponding to the rangevalue and the Doppler value.

In some demonstrative aspects, radar processor 836 may be configured todetermine a first one-dimensional (1D) AoA spectrum corresponding to afirst dimension of the Azimuth-Elevation domain, for example, based onthe radar Rx data 811, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine a second 1D AoA spectrum corresponding to a second dimensionof the Azimuth-Elevation domain, for example, based on the radar Rx data811, e.g., as described below.

In some demonstrative aspects, the first dimension of theAzimuth-Elevation domain may include an azimuth dimension, and thesecond dimension of the Azimuth-Elevation domain may include anelevation dimension, e.g., as described below.

In other aspects, the first dimension of the Azimuth-Elevation domainmay include the elevation dimension, and the second dimension of theAzimuth-Elevation domain may include the azimuth dimension.

In some demonstrative aspects, radar processor 836 may be configured todetermine the first 1D AoA spectrum and the second 1D AoA spectrum, forexample, according to a same spectrum analysis algorithm, e.g., asdescribed below.

In some demonstrative aspects, radar processor 836 may be configured todetermine the first 1D AoA spectrum, for example, according to a firstspectrum analysis algorithm, and to determine the second 1D AoAspectrum, for example, according to a second spectrum analysisalgorithm, which may be, for example, different from the first spectrumanalysis algorithm, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine the first 1D AoA spectrum and/or the second 1D AoA spectrum,for example, according to a super resolution spectrum analysisalgorithm, e.g., as described below.

In some demonstrative aspects, the super resolution spectrum analysisalgorithm may include a Minimum Variance Distortionless Response (MVDR)algorithm, a Minimum Power Distortionless Response (MPDR) algorithm, ora Multiple Signal Classification (MUSIC) algorithm.

In other aspects, the super resolution spectrum analysis algorithm mayinclude any other super resolution algorithm.

In some demonstrative aspects, radar processor 836 may be configured todetermine the first 1D AoA spectrum and/or the second 1D AoA spectrum,for example, based on a super resolution algorithm utilizing acovariance matrix, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine a particular 1D AoA spectrum corresponding to a particulardimension of the first dimension and/or the second dimension, forexample, by determining a covariance matrix corresponding to theparticular dimension, and determining the particular 1D AoA spectrumaccording to the super resolution spectrum analysis algorithm, forexample, based on the covariance matrix corresponding to the particulardimension, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine the covariance matrix corresponding to the particulardimension, for example, by applying to the radar Rx data 811 a SpatialSmoothing (SS) technique and/or a Forward-Backward (FB) technique, e.g.,as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine the covariance matrix corresponding to the particulardimension, for example, based on combined Rx data corresponding to aplurality of 1D antenna indexes in a first antenna dimension of the 2Dradar antenna, e.g., as described below.

In some demonstrative aspects, the combined Rx data corresponding to a1D antenna index in the first antenna dimension may be based on acombination of a plurality of data values in the radar Rx data 811,which correspond to the 1D antenna index in the first antenna dimension,e.g., as described below.

In some demonstrative aspects, the plurality of data values maycorrespond to a plurality of antenna indexes in a second antennadimension of the 2D radar antenna, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine the first 1D AoA spectrum and/or the second 1D AoA spectrum,for example, according to a delay-and-sum algorithm, e.g., as describedbelow.

In some demonstrative aspects, radar processor 836 may be configured todetermine a particular 1D AoA spectrum corresponding to a particulardimension of the first dimension and/or the second dimension bydetermining a plurality of intermediate 1D AoA spectrums correspondingto a respective plurality of subarrays in the particular dimension ofthe 2D radar antenna, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine the particular 1D AoA spectrum based on a combination of theplurality of intermediate 1D AoA spectrums, e.g., as described below.

In some demonstrative aspects, the particular dimension may include theazimuth dimension, and the plurality of subarrays may include aplurality of rows of the 2D radar antenna.

In some demonstrative aspects, the particular dimension may include theelevation dimension, and the plurality of subarrays may include aplurality of columns of the 2D radar antenna.

In some demonstrative aspects, radar processor 836 may be configured tocombine the plurality of intermediate 1D AoA spectrums, for example,such that the particular 1D AoA spectrum is generated with reduced, oreven no, SNR loss.

In one example, radar processor 836 may be configured to determine theparticular 1D AoA spectrum, by adding or multiplying the plurality ofintermediate 1D AoA spectrums.

In other aspects, radar processor 836 may be configured to determine thefirst 1D AoA spectrum and/or the second 1D AoA spectrum, for example,according to any other additional or alternative algorithm, methodand/or technique.

In some demonstrative aspects, radar processor 836 may be configured todetect one or more first object hypotheses in the first dimension, forexample, based on the first 1D AoA spectrum, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetect one or more second object hypotheses in the second dimension, forexample, based on the second 1D AoA spectrum, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine the one or more first object hypotheses and the one or moresecond object hypotheses, for example, according to a same objectdetection scheme, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine the one or more first object hypotheses, for example,according to a first object detection scheme, and to determine the oneor more second object hypotheses, for example, according to a secondobject detection scheme, which may be, for example, different from thefirst object detection scheme, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine a plurality of 2D object hypotheses corresponding to theAzimuth-Elevation domain, for example, based on the first objecthypotheses and the second object hypotheses, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine the plurality of 2D object hypotheses, for example, based on arespective plurality of different combinations of a hypothesis from thefirst object hypotheses and a hypothesis from the second objecthypotheses, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured togenerate the 2D AoA information, for example, based on a 2D AoA spectrumanalysis of the radar Rx data 811, for example, according to theplurality of 2D object hypotheses, e.g., as described below.

In some demonstrative aspects, the 2D AoA spectrum analysis may includea super resolution spectrum analysis algorithm, e.g., as describedbelow.

In some demonstrative aspects, the 2D AoA spectrum analysis may includea delay-and-sum algorithm, e.g., as described below.

In some demonstrative aspects, the 2D AoA spectrum analysis may includea maximum-likelihood (ML) algorithm, e.g., as described below.

In other aspects, the 2D AoA spectrum analysis may include any otheradditional or alternative algorithm, method and/or technique.

In some demonstrative aspects, radar processor 836 may be configured togenerate the 2D AoA information configured for a Field of View (FOV)parameter and/or a resolution parameter, e.g., as described below.

In some demonstrative aspects, a count of the plurality of 2D objecthypotheses may be less than 10% of a count of points in theAzimuth-Elevation domain, for example, according to the FOV parameterand the resolution parameter, e.g., as described below.

In some demonstrative aspects, the count of the plurality of 2D objecthypotheses may be less than 5% of the count of points in theAzimuth-Elevation domain, for example, according to the FOV parameterand the resolution parameter, e.g., as described below.

In some demonstrative aspects, the count of the plurality of 2D objecthypotheses may be less than 1% of the count of points in theAzimuth-Elevation domain, for example, according to the FOV parameterand the resolution parameter, e.g., as described below.

In other aspects, any other count of the plurality of 2D objecthypotheses may be implemented.

In some demonstrative aspects, radar processor 836 may be configured todetermine a selected AoA spectral points, for example, based on the 2DAoA spectrum analysis, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine a plurality of AoA spectral points, for example, based on the2D AoA spectrum analysis, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured toidentify one or more selected AoA spectral points from the plurality ofAoA spectral points, for example, based on a detection threshold, e.g.,as described below.

In some demonstrative aspects, radar processor 836 may be configured togenerate the 2D AoA information including 2D AoA informationcorresponding to the selected AoA spectral points, e.g., as describedbelow.

In some demonstrative aspects, radar processor 836 may be configured todynamically set the detection threshold, for example, based on one ormore detection criteria, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine a candidate set of 2D object hypotheses, for example, based onthe first object hypotheses and the second object hypotheses, e.g., asdescribed below.

In some demonstrative aspects, radar processor 836 may be configured todetermine a reduced candidate set of 2D object hypotheses by excludingone or more 2D object hypotheses from the candidate set of 2D objecthypotheses, for example, based on at least one exclusion criterion,e.g., as described below.

In one example, the exclusion criterion may be based, for example, ongeometrical considerations.

In another example, the exclusion criterion may be based, for example,on one or more regions of the Azimuth-Elevation domain, e.g., one ormore Regions of Interest (ROIs).

In another example, any other exclusion criterion may be implemented.

In some demonstrative aspects, radar processor 836 may be configured togenerate the 2D AoA information, for example, based on the 2D AoAspectrum analysis of the radar Rx data 811 according to the reducedcandidate set of 2D object hypotheses, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured toidentify one or more regions of the Azimuth-Elevation domain, forexample, the ROIs, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine at least one 1D AoA spectrum of the first 1D AoA spectrumand/or the second 1D AoA spectrum, for example, in only the one or moreregions of the Azimuth-Elevation domain, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured toidentify the one or more regions, for example, based on a coarse AoAspectrum analysis of the Rx radar data 811, e.g., as described below.

In one example, the coarse AoA spectrum analysis may include a 1D coarseAoA spectrum analysis corresponding to a dimension of theAzimuth-Elevation domain.

In one example, the coarse AoA spectrum analysis may include a 2D coarseAoA spectrum analysis corresponding to the Azimuth-Elevation domain.

In some demonstrative aspects, a resolution of the coarse AoA spectrumanalysis may be lower than a resolution of the 1D AoA spectrum, e.g., asdescribed below.

In some demonstrative aspects, radar processor 836 may be configured toidentify the one or more regions, for example, based on map information,scene occlusions, a planned maneuver, a trajectory, and/or a Transmit(Tx) Beamforming (BF) configuration, e.g., as described below.

In one example, the map information may include high definition mapinformation.

In one example, the Tx BF configuration may define one or more parts ofthe FOV, to which energy may be focused, for example, by applying aspatial filter. Accordingly, radar processor 836 may be configured toidentify the one or more regions to include regions of theAzimuth-Elevation domain, to which the energy is focused based on the TxBF configuration, for example, while excluding parts of the FOV, towhich the energy is not focused according to the Tx BF configuration.

In some demonstrative aspects, radar processor 836 may be configured toidentify the one or more regions, for example, based on information fromhigher layers and/or applications e.g., of radar device 101 (FIG. 1).

In some demonstrative aspects, radar processor 836 may be configured toidentify the one or more regions, for example, based on a plannedmaneuver or a trajectory, e.g., of vehicle 100 (FIG. 1).

In some demonstrative aspects, the coarse AoA spectrum analysis may beoptional. For example, radar processor 836 may determine the 1D AoAspectrums of the first 1D AoA spectrum and/or the second 1D AoAspectrum, for example, even without performing the coarse AoA spectrumanalysis.

In some demonstrative aspects, radar processor 836 may be configured todetermine the first 1D AoA spectrum, the second 1D AoA spectrum, and/orthe 2D AoA information, for example, according to a predefinedcoordinate system, e.g., as described below.

In some demonstrative aspects, radar processor 836 may be configured todetermine the first 1D AoA spectrum, the second 1D AoA spectrum, and/orthe 2D AoA information for example, according to a coupled coordinatesystem, e.g., as described below.

In one example, the coupled coordinate system may include a degree-basedcoordinate system.

In another example, the coupled coordinate system may include any othercoordinate system.

In some demonstrative aspects, radar processor 836 may be configured todetermine the second 1D AoA spectrum based on the one or more firstobject hypotheses in the first dimension based on the first 1D AoAspectrum, e.g., as described below.

In one example, radar processor 836 may be configured to determine thesecond 1D AoA spectrum based on the one or more first object hypothesesin the first dimension.

In one example, there may be a coupling between values in the firstdimension and values in the second dimension of the coupled coordinatesystem. For example, due to this coupling the first and second 1D AoAspectrums may be determined sequentially, for example, by determiningthe first 1D AoA spectrum, and then determining the second 1D AoAspectrum based on the first 1D AoA spectrum.

In one example, the 1D AoA spectrum corresponding to the azimuth domainmay be determined, followed by determining the 1D AoA spectrumcorresponding to the elevation domain, for example, based on the 1D AoAspectrum corresponding to the azimuth domain. In another example, the 1DAoA spectrum corresponding to the elevation domain may be determined,followed by determining the 1D AoA spectrum corresponding to the azimuthdomain, for example, based on the 1D AoA spectrum corresponding to theelevation domain.

In some demonstrative aspects, radar processor 836 may be configured todetermine the first 1D AoA spectrum, the second 1D AoA spectrum, and/orthe 2D AoA information, for example, according to a non-coupledcoordinate system, e.g., as described below.

In one example, the non-coupled coordinate system may include a UVcoordinate system, e.g., as described below.

In another example, any other non-coupled coordinate system may be used.

In one example, values of the first dimension and values in the seconddimension may be independent from another, for example, when representedin the non-coupled coordinate system. For example, there may be nocoupling between values in the first dimension and values in the seconddimension. For example, this may support determining the first 1D AoAspectrum independent from determining the second 1D AoA spectrum.Accordingly, the first 1D AoA spectrum and the second AoA spectrum maybe determined in parallel, or partially in parallel.

In one example, a target, denoted T, may be represented by a point in acoordinate system (X, Y, Z), where the X-axis may point to the right andmay represents the azimuth dimension, the Y-axis may point forward andmay represent the boresight, and the Z-axis may point up and mayrepresent the elevation dimension. For example, a radar array may lie inthe X-Z plane, and a zero value may be at the boresight. According tothis example, the target T may be represented by the coordinates T(R, θ,φ), wherein R denotes a range of the target from the origin, θ denotesan angle of a projection of R on the X-Y plane, and φ denotes an angleof a projection of R on the Z-Y plane.

In some demonstrative aspects, the projection of the target T on the X-Yplane may be represented by Rcos(φ)sin(θ), and/or the projection thetarget T on the Z-Y plane may be represented by Rsin(φ).

In some demonstrative aspects, the range R may be known, for example,according to a range-Doppler bin of an AoA. Accordingly, an azimuthvector, denoted “Az_Vector”, and an elevation vector, denoted“El_Vector”, of the target T may be determined, e.g., as follows:

Az_Vector=cos(φ)sin(θ),

El_Vector=sin(φ).

In some demonstrative aspects, a uniform grid may be set for the anglesφ and θ, for example, in a coupled coordinate system, e.g., adegree-based coordinate system, for example, φ=−30:0.5:30 andθ=−60:0.5:60.

In some demonstrative aspects, a uniform grid in the trigonometricdomain may be set for the angles φ and θ, for example, in a non-coupledcoordinate system, e.g., a UV-based coordinate system, e.g.,u=cos(φ)sin(θ), v=sin(φ). Accordingly, u=−1:0.05:1 and v=−1:0.05:1,e.g., may be equal to a +/−90 degree FOV, or u=−0.8:0.05:0.8 andv=−0.5:0.05:0.5, e.g., may be used for a FOV less than +/−90 degrees.

In some demonstrative aspects, the UV coordinate system may provide alarger coordinate spacing, for example, when approaching edges of theFOV, e.g., where a radar is less accurate, which may result in a reducednumber of hypotheses to check.

In some demonstrative aspects, the UV coordinate system may result in alinear term in a steering vector, e.g., the term exp( ), which mayreduce complexity, for example, in a Fast Fourier Transform (FFT) and/orother implementations and/or computations using the steering vector.

Reference is made to FIG. 9, which schematically illustrates a method ofgenerating 2D AoA information, in accordance with some demonstrativeaspects. For example, one or more of the operations of the method ofFIG. 9 may be performed by a radar processor, e.g., radar processor 836(FIG. 8).

In some demonstrative aspects, as indicated at block 902, the method mayinclude performing a coarse AoA spectrum analysis on radar Rx data 901,for example, over a FOV, e.g., over an entire FOV, for example, toprovide a coarse AoA Spectrum 903, e.g., of a FOV. For example, radarprocessor 834 (FIG. 8) may apply the coarse AoA spectrum analysis to theradar Rx data 811 (FIG. 8), for example, to output the coarse AoASpectrum 903, e.g., as described above.

In one example, the coarse AoA Spectrum 903 may have a reduced or alower resolution, e.g., compared to the 1D AoA spectrum described above.

In some demonstrative aspects, the coarse AoA spectrum analysis of theFOV may include, for example, a Windowed Bartlett Beamforming (BF).

In some demonstrative aspects, the coarse AoA spectrum analysis of theFOV may include, for example, applying a super resolution algorithm to asub array of a radar antenna. For example, applying the super resolutionalgorithm to the sub array may result in reduced complexity.

In some demonstrative aspects, the coarse AoA spectrum analysis of theFOV may include, for example, applying a super resolution algorithm tothe sub array, for example, according to a reduced hypotheses grid,which may result in a further reduction of the complexity.

In some demonstrative aspects, the super resolution algorithm may besuitable, for example, for a sparse array topology.

In some demonstrative aspects, the super resolution algorithm mayinclude, for example, a MUSIC algorithm, an Amplitude and PhaseEStimation (APES) algorithm, an MVDR algorithm, and/or any other superresolution algorithm.

In one example, a spatial Smoothing technique and/or a Forward-Backwardtechnique may be implemented for the one or more super-resolutionalgorithms, e.g., the MUSIC algorithm and/or the MVDR algorithm, forexample, for supporting single-shot processing, e.g., as describedbelow.

In some demonstrative aspects, the coarse AoA spectrum analysis of theFOV may include, for example, a Beam-Space analysis.

In some demonstrative aspects, the coarse AoA spectrum analysis may beoptional, and may include a coarse scan of an entire FOV, for example,to detect parts of the FOV, e.g., ROIs, that may be subject for furthersuper resolution processing, e.g., as described below.

In some demonstrative aspects, the coarse AoA spectrum analysis of theFOV may be based, for example, on information on one or more regions,e.g., ROIs, for example, from upper layers and/or applications, e.g., ofradar device 101 (FIG. 1).

In some demonstrative aspects, as indicated at block 904, the method mayinclude detecting one or more regions 905 of the coarse AoA Spectrum903. For example, radar processor 834 (FIG. 8) may identify the one ormore regions 905 based on the coarse AoA spectrum analysis.

In some demonstrative aspects, radar processor 834 (FIG. 8) maydetermine the one or more regions 905, for example, using a signalversus noise detector, a Generalized Likelihood Ratio Test (GLRT),and/or any other peak detector.

In some demonstrative aspects, as indicated at block 906, the method mayinclude determining a first 1D AoA spectrum 907 corresponding to a firstdimension of the Azimuth-Elevation domain, e.g., the azimuth domain,according to the one or more regions 905. For example, radar processor834 (FIG. 8) may determine the first 1D AoA spectrum 907 according tothe one or more regions 905.

In some demonstrative aspects, blocks 902 and 904 may be optional. Forexample, in some implementations, the method may be operated to being atblock 906. For example, radar processor 834 (FIG. 8) may determine thefirst 1D AoA spectrum 907 based on radar Rx data 901, e.g., with respectto the entire FOV.

In one example, determining the first 1D AoA spectrum 907 may leveragemeasurements of an entire array. This may be done on an entire FOV orjust on coordinates, e.g., angle coordinates and/or UV coordinates,within the ROIs,

In some demonstrative aspects, radar processor 834 (FIG. 8) maydetermine the first 1D AoA spectrum 907, for example, by processing theRx data 811 over the entire array, e.g., instead of row by row or columnby column.

In some demonstrative aspects, radar processor 834 (FIG. 8) maydetermine the first 1D AoA spectrum 907, for example, based on an AoAspectrum analysis algorithm.

In one example, radar processor 834 (FIG. 8) may determine the first 1DAoA spectrum 907, for example, based on a super resolution algorithm,which may provide improved performance.

In one example, a non-iterative super resolution algorithm, e.g., aMUSIC algorithm or the like, may be preferred, for example, to reducecomplexity.

In some demonstrative aspects, radar processor 834 (FIG. 8) may applythe super resolution algorithm over the entire array, for example,instead of by row or column by column, e.g., to retain an SNR. However,a reduced steering vector size of the super resolution algorithm may beused, e.g., to reduce computation complexity.

In some demonstrative aspects, radar processor 834 (FIG. 8) may beconfigured to apply a 2D to 1D reduction with respect to the radar Rxdata 811, which includes an entire 2D array measurement.

For example, radar processor 834 (FIG. 8) may apply the 2D to 1Dreduction by applying to the radar Rx data 811 a Spatial Smoothingtechnique and/or the Forward-Backward technique, for example, to build acovariance matrix to be used in the super-resolution algorithm, e.g.,the MUSIC algorithm, a Minimum-Power Distortionless Response (MPDR)algorithm and/or any other SR algorithm.

In some demonstrative aspects, the Spatial Smoothing (SS) and/orForward-Backward (FB) (SSFB) techniques may be suitable for a symmetricand uniform or quasi-uniform antenna array.

In one example, the SSFB may include one or more of the followingoperations:

-   -   Assume that the 1D AoA spectrum (azimuth) is along array lines        of measurements of a radar antenna.    -   Select an SS order, e.g., a size of a sub-array, for example,        according to fixed setting, heuristics, or dynamically, e.g.,        through an evaluation of a signal space size.    -   Perform an SSFB method for each line, yielding SSFB(i) matrix        for line i. an SSFB(i) matrix size may be equal to (sub-array        steering vector length)*(2*number of sub-arrays)    -   A matrix, denoted R_sub, may be a concatenation of SSFB(i)        matrices of all lines, e.g., as follows:        -   R_sub=[SSFB(1), SSFB(2), . . . , SSFB(N)], where N is a            number of lines in the array    -   Multiply the matrix R_sub with itself to determine a        measurements-based estimation for a Covariance matrix R, e.g.,        R=R_sub*R_sub^(H), i.e., R_sub*Hermitian(R_sub)    -   The covariance matrix R may be used, e.g., after inversion in a        super resolution algorithm, e.g., the MUSIC algorithm, the MPDR        algorithm, or any other non-iterative super resolution algorithm        having 2D measurements information.

In other aspects, the SSFB may include any other additional oralternative operations.

In some demonstrative aspects, as indicated at block 908, the method mayinclude detecting one or more first object hypotheses 909 in the firstdimension (azimuth) based on the first 1D AoA spectrum 907. For example,radar processor 834 (FIG. 8) may detect the one or more first objecthypotheses 909 based on the first 1D AoA spectrum 907.

In one example, radar processor 834 (FIG. 8) may determine the one ormore first object hypotheses 909, for example, according to a peakdetector.

In some demonstrative aspects, as indicated at block 910, the method mayinclude determining a second 1D AoA spectrum 911 corresponding to asecond dimension of the Azimuth-Elevation domain, e.g., the elevationdomain, for example, based on the Radar Rx data, e.g., over the one ormore regions 905, or over the entire FOV. For example, radar processor834 (FIG. 8) may determine the second 1D AoA spectrum 911.

In one example, determining the second 1D AoA spectrum 911 may leveragemeasurements of an entire array. This may be done on an entire FOV orjust on coordinates, e.g., angle coordinates and/or UV coordinates,within the ROIs,

In some demonstrative aspects, radar processor 834 (FIG. 8) maydetermine the second 1D AoA spectrum 911, for example, by processing theentire array, e.g., instead of row by row or column by column.

In some demonstrative aspects, blocks 902 and 904 may be optional. Forexample, in some aspects, radar processor 834 (FIG. 8) may determine thesecond 1D AoA spectrum 911 based on radar Rx data 901 with respect tothe entire FOV.

In some demonstrative aspects, radar processor 834 (FIG. 8) maydetermine the second 1D AoA spectrum 911, for example, based on an AoAspectrum analysis algorithm.

In some demonstrative aspects, radar processor 834 (FIG. 8) maydetermine the second 1D AoA spectrum 911, for example, using a same AoAspectrum analysis algorithm used to determine the first 1D AoA spectrum907.

In some demonstrative aspects, radar processor 834 (FIG. 8) maydetermine the second 1D AoA spectrum 911, for example, using a differentAoA spectrum analysis algorithm from the AoA spectrum analysis algorithmused to determine the first 1D AoA spectrum 907.

In one example, the AoA spectrum analysis algorithm for the second 1DAoA spectrum 911 may be different from the AoA spectrum analysisalgorithm for the first 1D AoA spectrum 907, for example, with respectto a Hypothesis grid resolution, an SS order selected method and/orvalue, and/or with respect to any other parameters and/or methods.

In some demonstrative aspects, as indicated at block 912, the method mayinclude detecting one or more second object hypotheses 913 in the seconddimension (elevation), for example, based on the second 1D AoA spectrum911. For example, radar processor 834 (FIG. 8) may detect the one ormore second object hypotheses 913 based on the second 1D AoA spectrum911.

In one example, radar processor 834 (FIG. 8) may determine the one ormore first object hypotheses 909, for example, using a peak detector.

In some demonstrative aspects, radar processor 834 (FIG. 8) maydetermine the one or more second object hypotheses 913, for example,using a same peak detector used to detect the one or more first objecthypotheses 909.

In some demonstrative aspects, radar processor 834 (FIG. 8) maydetermine the one or more second object hypotheses 913, for example,using a different peak detector from the peak detector used to detectthe one or more first object hypotheses 909.

In some demonstrative aspects, radar processor 834 (FIG. 8) maydetermine the one or more second object hypotheses 913, for example,based on a combination of a peak detector followed by a signal versusnoise detector, e.g., a GLRT detector, and/or any other detector.

In some demonstrative aspects, the one or more second object hypotheses913 and the one or more first object hypotheses 909 may be used ascandidates for a reduced candidate set of 2D object hypotheses, e.g., asdescribed below.

In some demonstrative aspects, as indicated at block 914, the method mayinclude determining a reduced candidate set of 2D object hypotheses 915based on the first object hypotheses 909 and the second objecthypotheses 913.

For example, radar processor 834 (FIG. 8) may determine a candidate setof 2D object hypotheses based on the first object hypotheses 909 and thesecond object hypotheses 913, and may determine the reduced candidateset of 2D object hypotheses 915 by excluding one or more 2D objecthypotheses from the candidate set of 2D object hypotheses based on atleast one exclusion criterion, e.g., as described below.

In some demonstrative aspects, radar processor 834 (FIG. 8) maydetermine the reduced candidate set of 2D object hypotheses, forexample, by removing 2D object hypotheses that may be outside the FOV.

In some demonstrative aspects, radar processor 834 (FIG. 8) maydetermine the reduced candidate set of 2D object hypotheses, forexample, by removing 2D object hypotheses that may not be aligned withthe coarse analysis phase 902, e.g., hypotheses which are outside theone or more regions 905.

In some demonstrative aspects, radar processor 834 (FIG. 8) maydetermine the reduced candidate set of 2D object hypotheses, forexample, by removing angle hypotheses that may not be possible due toone or more criteria, e.g., with respect to a specific platform, withrespect to an antenna design, with respect to a beam pattern of antennaelements, with respect to an application usage, according to higherlayers directives and/or any similar heuristics or system constraints.

In one example, the candidate set of 2D object hypotheses may beprocessed to remove ambiguity, for example, by a conditional joint 2Dprocessing, for example, only on the candidate set of 2D objecthypotheses and a resulting signal strength evaluation.

Reference is made to FIG. 10A, which schematically illustrates a first1D AoA spectrum 1010, and to FIG. 10B, which schematically illustrates asecond 1D AoA spectrum 1020, in accordance with some demonstrativeaspects.

In some demonstrative aspects, first 1D AoA spectrum 1010 may correspondto the Azimuth dimension.

In one example, radar processor 834 (FIG. 8) may determine one or moreobject hypotheses 1012 based on detected peaks in the 1D AoA spectrum1010. For example, the one or more object hypotheses 1012 may representthe one or more first object hypotheses 909 (FIG. 9).

In some demonstrative aspects, second 1D AoA spectrum 1020 maycorrespond to the elevation dimension.

In one example, radar processor 834 (FIG. 8) may determine one or moreobject hypotheses 1022 based on detected peaks in the 1D AoA spectrum1020. For example, the one or more object hypotheses 1022 may representthe one or more second object hypotheses 913 (FIG. 9).

In some demonstrative aspects, radar processor 834 (FIG. 8) maydetermine a plurality of 2D object hypotheses, e.g., plurality of 2Dobject hypotheses based on a respective plurality of differentcombinations of a hypothesis 1012 from the first object hypotheses 1012and a hypothesis 1022 from the second object hypotheses 1022.

In one example, as shown in FIG. 10A, the one or more object hypotheses1012 may include 4 object hypotheses; and, as shown in FIG. 10B, the oneor more object hypotheses 1022 may include 4 object hypotheses.According to this example, the candidate set of 2D object hypotheses mayinclude 16 2D object hypotheses, for example, based on all possible 16combinations between the 4 object hypotheses 1012 and the 4 hypotheses1022.

Referring back to FIG. 9, in some demonstrative aspects, the operationsof blocks 906, 908, 910 and/or 912, may be performed, for example, withrespect to a data representation in a non-coupled coordinate system,e.g., the UV coordinate system, or any other non-coupled coordinatesystem.

In some demonstrative aspects, the operations of blocks 906, 908, 910and/or 912, may be performed, for example, with respect to a datarepresentation in a coupled coordinate system, e.g., a degree-basedcoordinate system.

In some demonstrative aspects, the operations of blocks 910 and 912 maybe performed independently from, and/or in parallel to, the operationsof blocks 906 and 908, for example, when utilizing the non-coupledcoordinate system or the coupled coordinate system.

In some demonstrative aspects, the operations of blocks 910 and 912 maybe sequentially performed after the operations of blocks 906 and 908,for example, when utilizing the coupled coordinate system or thenon-coupled coordinate system.

In one example, there may be a coupling between Elevation and Azimuthangles, for example, in the degree-based coordinate system, e.g., asopposed to the UV coordinate system.

In some demonstrative aspects, an angle compensation may be determined,for example, with respect to the degree-based coordinate system. Forexample, this angle compensation may not be applied with respect to theUV coordinate system.

In one example, when an estimated azimuth vector is represented asAz_vec=cos(φ)sin(θ), the term sin⁻¹(Az_Vec) may not be equal to theangle θ. According to this example, it may be preferred to determinesin⁻¹(Az_Vec/cos(φ)), for example, in order to determine an estimate ofθ.

In one example, if φ is small and around the origin, it may be assumedthat cos(φ)˜1. For example, cos(φ) may be estimated fromcos(sin⁻¹(El_vec)).

In some demonstrative aspects, as indicated at block 916, the method mayinclude performing a 2D AoA spectrum analysis of the radar Rx data 901,for example, according to the reduced candidate set of 2D objecthypotheses 915, to generate a plurality of AoA spectral points 917. Forexample, radar processor 834 (FIG. 8) may generate the plurality of AoAspectral points 917, for example, based on the 2D AoA spectrum analysis916 of the radar Rx data 901 according to the reduced candidate set of2D object hypotheses 915, e.g., as described below.

In one example, radar processor 834 (FIG. 8) may generate the pluralityof AoA spectral points 917, for example, by applying a 2D AoA spectrumanalysis method to an entire 2D array for an estimation, which may beequal to or better than a 1D AoA estimation method used to estimate thefirst 1D AoA spectrum and/or the second 1D AoA spectrum. For example,using a method with reduced performance compared to the 1D AoAestimation method may result in degraded overall performance.

In some demonstrative aspects, radar processor 834 (FIG. 8) may generatethe plurality of AoA spectral points 917, for example, by applying a 2Dsuper resolution algorithm, e.g., using a larger array aperture than the1D AoA estimation method, for example, when a sub-array is used toestimate the first 1D AoA spectrum and/or the second 1D AoA spectrum.

In some demonstrative aspects, radar processor 834 (FIG. 8) may generatethe plurality of AoA spectral points 917, for example, based on aMaximum Likelihood (ML) signal estimation, e.g., as described above.

In one example, the ML signal estimation may be optimal for un-biasedestimators, and, therefore may be preferred in some use cases. In otheraspects, any other 2D AoA estimation method may be applied.

In one example, in some use cases, the ML signal estimation may beadvantageous, e.g., compared to other 2D AoA spectrum analysis methods,for example, since a detection magnitude may be optimally estimated bythe signal estimation.

In another example, the ML signal estimation may output an optimalestimation of a target reflection signal, and, accordingly, soft valuesmay be delivered to a detector and/or higher layers or for one or morehigh-level applications, e.g., better processing, a track before detect,Artificial Intelligence (AI) classification and tracking, and/or thelike.

In some demonstrative aspects, as indicated at block 918, the method mayinclude identifying one or more selected AoA spectral points from theplurality of AoA spectral points 917 based on a detection threshold togenerate 2D AoA information 919. For example, radar processor 834 (FIG.8) may identify the one or more selected AoA spectral points of theplurality of AoA spectral points 917 based on a detection threshold, andmay generate the 2D AoA information 919 including 2D AoA informationcorresponding to the selected AoA spectral points, e.g., as describedabove.

In some demonstrative aspects, radar processor 834 (FIG. 8) may generatethe 2D AoA information 919, for example, based on a noise varianceestimation, e.g., using a final detector.

In some demonstrative aspects, the final detector may be optional and/ormay be performed by higher layers of radar device 101 (FIG. 1).

In some demonstrative aspects, radar processor 834 (FIG. 8) may approveor reject as a ghost ambiguity one or more of the plurality of AoAspectral points 917.

In some demonstrative aspects, the 2D AoA information 919 of theselected AoA spectral points may include magnitudes and/or coordinatesof detected signals.

In some demonstrative aspects, the 2D AoA information 919 may include,for example, an AoA detection list, for example, in opposed to a 2D AoAspectrum.

In some demonstrative aspects, radar processor 834 (FIG. 8) may generatethe 2D AoA information 919, for example, based on a coarse 2D AoAestimation, for example, if targets are sparse in the FOV.

In one example, a coarse AoA analysis, e.g., as described above withreference to block 902, may be performed, for example, before generatingthe 2D AoA information 919, for example, to reduce the candidate listsize. For example, the coarse AoA analysis may be performed beforegenerating the 2D AoA information 919, for example, in addition to, orinstead of, the AoA coarse analysis before the operation of block 904.

In some demonstrative aspects, radar processor 834 (FIG. 8) may generatethe 2D AoA information 919, for example, based on any other additionalor alternative algorithm, or a combination of the 2D AoA estimationalgorithms described above.

In some demonstrative aspects, radar processor 834 (FIG. 8) may producethe candidate set of the plurality of AoA spectral points 917, e.g.,with an ambiguity, and, may then remove ambiguous results from the listto generate the 2D AoA information 919.

In some demonstrative aspects, the method described above may provideone or more advantages over other 2D AoA spectrum analysis methods,e.g., as described below.

In one example, the 2D AoA estimation method described above may providean increased 2D resolution performance and a low complexity, forexample, which may be similar to a classical Bartlett Beam Formingfiltering.

In another example, the 2D AoA estimation method described above may besuitable for sparse arrays, e.g., including sets, clusters or groups(sub arrays) having a same topology. According to this example, thecoarse AoA analysis may be performed based on a coarse MVDR or othersuper resolution method, e.g., as opposed to a Bartlett BF method.

In another example, the 2D AoA estimation method described above may besuitable for any other antenna array, for example, a widely separatedarray and/or a multi-static radar system, for example, where relativephase information may be resolved.

In some demonstrative aspects, the 2D AoA estimation method describedabove may support an implementation with reduced computationalcomplexity, for example, compared to other 2D AoA spectrum analysismethods, e.g., as described below.

In some demonstrative aspects, the reduced computational complexity maybe achieved, for example, based on the ability to perform superresolution processing, e.g., at blocks 906, 910 and 916, for example,only on areas of interest of the FOV, e.g., the ROIs, for example,instead of the entire FOV.

In some demonstrative aspects, the reduced computational complexity maybe achieved, for example, based on the ability to perform the operationsindicated at blocks 906, 910 and 916, for example even withoutiterations.

In some demonstrative aspects, the reduced computational complexity maybe achieved, for example, based on the ability to perform separablephases, each phase including only a single matrix inversion.

In some demonstrative aspects, the reduced computational complexity maybe achieved, for example, based on the ability to perform the 1D AoAprocessing, for example, at blocks 906 and 910, e.g., once perdimension.

In some demonstrative aspects, the reduced computational complexity maybe achieved, for example, based on the ability to perform the MLrefinement process, at block 916, for example, on a relatively smallnumber of AoA candidates, e.g., the selected AoA spectral points and/orthe plurality of AoA spectral points 917.

Reference is made to FIG. 11, which schematically illustrates a methodof generating 2D AoA information, in accordance with some demonstrativeaspects. For example, one or more of the operations of the method ofFIG. 11 may be performed by a radar processor, e.g., radar processor 836(FIG. 8).

As indicated at block 1102, the method may include receiving radar Rxdata, the radar Rx data based on radar signals of a 2D radar antenna.For example, radar processor 836 (FIG. 8) may receive, e.g., via input832 (FIG. 8), the radar Rx data 811 (FIG. 8), which is based on theradar signals received via radar antenna 881 (FIG. 8), e.g., asdescribed above.

As indicated at block 1104, the method may include generating radarinformation based on the radar Rx data, the radar information including2D AoA information in an Azimuth-Elevation domain. For example, radarprocessor 836 (FIG. 8) may generate radar information 813 (FIG. 8)including the 2D AoA information in the Azimuth-Elevation domain, e.g.,as described above.

As indicated at block 1106, generating the radar information may includedetermining a first 1D AoA spectrum corresponding to a first dimensionof the Azimuth-Elevation domain based on the radar Rx data. For example,radar processor 836 (FIG. 8) may determine the first 1D AoA spectrumcorresponding to the first dimension of the Azimuth-Elevation domainbased on the radar Rx data 811 (FIG. 8), e.g., as described above.

As indicated at block 1108, generating the radar information may includedetecting one or more first object hypotheses in the first dimensionbased on the first 1D AoA spectrum. For example, radar processor 836(FIG. 8) may detect the one or more first object hypotheses in the firstdimension based on the first 1D AoA spectrum, e.g., as described above.

As indicated at block 1110, generating the radar information may includedetermining a second 1D AoA spectrum corresponding to a second dimensionof the Azimuth-Elevation domain based on the radar Rx data. For example,radar processor 836 (FIG. 8) may determine the second 1D AoA spectrumcorresponding to the second dimension of the Azimuth-Elevation domainbased on the radar Rx data 811 (FIG. 8), e.g., as described above. Inother aspects, the second 1D AoA spectrum may be determinedindependently from, e.g., in parallel to, the determination of the first1D AoA spectrum, e.g., as described above. In other aspects, the second1D AoA spectrum may be determined based on the first 1D AoA spectrum,e.g., as described above.

As indicated at block 1112, generating the radar information may includedetecting one or more second object hypotheses in the second dimensionbased on the second 1D AoA spectrum. For example, radar processor 836(FIG. 8) may detect the one or more second object hypotheses in thesecond dimension based on the second 1D AoA spectrum, e.g., as describedabove.

As indicated at block 1114, generating the radar information may includedetermining a plurality of 2D object hypotheses corresponding to theAzimuth-Elevation domain based on the first object hypotheses and thesecond object hypotheses. For example, radar processor 836 (FIG. 8) maydetermine the plurality of 2D object hypotheses corresponding to theAzimuth-Elevation domain based on the first object hypotheses and thesecond object hypotheses, e.g., as described above.

As indicated at block 1116, generating the radar information may includegenerating the 2D AoA information based on a 2D AoA spectrum analysis ofthe radar Rx data according to the plurality of 2D object hypotheses.For example, radar processor 836 (FIG. 8) may generate the 2D AoAinformation based on the 2D AoA spectrum analysis of the radar Rx data811 (FIG. 8) according to the plurality of 2D object hypotheses, e.g.,as described above.

Reference is made to FIG. 1212, which schematically illustrates aproduct of manufacture 1200, in accordance with some demonstrativeaspects. Product 1200 may include one or more tangible computer-readable(“machine-readable”) non-transitory storage media 1202, which mayinclude computer-executable instructions, e.g., implemented by logic1204, operable to, when executed by at least one computer processor,enable the at least one computer processor to implement one or moreoperations and/or functionalities of radar processor 834 (FIG. 8), oneor more operations and/or functionalities described with reference tothe FIGS. 1-11, and/or one or more operations described herein. Thephrases “non-transitory machine-readable medium” and “computer-readablenon-transitory storage media” may be directed to include all machineand/or computer readable media, with the sole exception being atransitory propagating signal.

In some demonstrative aspects, product 1200 and/or storage media 1202may include one or more types of computer-readable storage media capableof storing data, including volatile memory, non-volatile memory,removable or non-removable memory, erasable or non-erasable memory,writeable or re-writeable memory, and the like. For example, storagemedia 1202 may include, RAM, DRAM, Double-Data-Rate DRAM (DDR-DRAM),SDRAM, static RAM (SRAM), ROM, programmable ROM (PROM), erasableprogrammable ROM (EPROM), electrically erasable programmable ROM(EEPROM), Compact Disk ROM (CD-ROM), Compact Disk Recordable (CD-R),Compact Disk Rewriteable (CD-RW), flash memory (e.g., NOR or NAND flashmemory), content addressable memory (CAM), polymer memory, phase-changememory, ferroelectric memory, silicon-oxide-nitride-oxide-silicon(SONOS) memory, a disk, a floppy disk, a hard drive, an optical disk, amagnetic disk, a card, a magnetic card, an optical card, a tape, acassette, and the like. The computer-readable storage media may includeany suitable media involved with downloading or transferring a computerprogram from a remote computer to a requesting computer carried by datasignals embodied in a carrier wave or other propagation medium through acommunication link, e.g., a modem, radio or network connection.

In some demonstrative aspects, logic 1204 may include instructions,data, and/or code, which, if executed by a machine, may cause themachine to perform a method, process, and/or operations as describedherein. The machine may include, for example, any suitable processingplatform, computing platform, computing device, processing device,computing system, processing system, computer, processor, or the like,and may be implemented using any suitable combination of hardware,software, firmware, and the like.

In some demonstrative aspects, logic 1204 may include, or may beimplemented as, software, a software module, an application, a program,a subroutine, instructions, an instruction set, computing code, words,values, symbols, and the like. The instructions may include any suitabletype of code, such as source code, compiled code, interpreted code,executable code, static code, dynamic code, and the like. Theinstructions may be implemented according to a predefined computerlanguage, manner, or syntax, for instructing a processor to perform acertain function. The instructions may be implemented using any suitablehigh-level, low-level, object-oriented, visual, compiled and/orinterpreted programming language, such as C, C++, Java, BASIC, Matlab,Pascal, Visual BASIC, assembly language, machine code, and the like.

EXAMPLES

The following examples pertain to further aspects.

Example 1 includes an apparatus comprising a radar processor, the radarprocessor comprising an input to receive radar receive (Rx) data, theradar Rx data based on radar signals of a Two Dimensional (2D) radarantenna; and a processor to generate radar information based on theradar Rx data, the radar information comprising 2D Angle of Arrival(AoA) information in an Azimuth-Elevation domain, wherein the processoris configured to determine a first one-dimensional (1D) AoA spectrumcorresponding to a first dimension of the Azimuth-Elevation domain basedon the radar Rx data, to determine a second 1D AoA spectrumcorresponding to a second dimension of the Azimuth-Elevation domainbased on the radar Rx data, to detect one or more first objecthypotheses in the first dimension based on the first 1D AoA spectrum, todetect one or more second object hypotheses in the second dimensionbased on the second 1D AoA spectrum, to determine a plurality of 2Dobject hypotheses corresponding to the Azimuth-Elevation domain based onthe first object hypotheses and the second object hypotheses, and togenerate the 2D AoA information based on a 2D AoA spectrum analysis ofthe radar Rx data according to the plurality of 2D object hypotheses.

Example 2 includes the subject matter of Example 1, and optionally,wherein the radar processor is configured to determine a candidate setof 2D object hypotheses based on the first object hypotheses and thesecond object hypotheses, to determine a reduced candidate set of 2Dobject hypotheses by excluding one or more 2D object hypotheses from thecandidate set of 2D object hypotheses based on at least one exclusioncriterion, and to generate the 2D AoA information based on the 2D AoAspectrum analysis of the radar Rx data according to the reducedcandidate set of 2D object hypotheses.

Example 3 includes the subject matter of Example 1 or 2, and optionally,wherein the radar processor is configured to identify one or moreregions of the Azimuth-Elevation domain, and to determine at least one1D AoA spectrum of the first 1D AoA spectrum or the second 1D AoAspectrum in, e.g., in only, the one or more regions of theAzimuth-Elevation domain.

Example 4 includes the subject matter of Example 3, and optionally,wherein the radar processor is configured to identify the one or moreregions based on a coarse AoA spectrum analysis of the Rx radar data, aresolution of the coarse AoA spectrum analysis is lower than aresolution of the at least one 1D AoA spectrum.

Example 5 includes the subject matter of Example 3 or 4, and optionally,wherein the radar processor is configured to identify the one or moreregions based on at least one of map information, scene occlusions, aplanned maneuver, a trajectory or a Transmit (TX) Beamforming (BF)configuration.

Example 6 includes the subject matter of any one of Examples 1-5, andoptionally, wherein the radar processor is configured to determine aplurality of AoA spectral points based on the 2D AoA spectrum analysis,to identify one or more selected AoA spectral points from the pluralityof AoA spectral points based on a detection threshold, and to generatethe 2D AoA information comprising 2D AoA information corresponding tothe selected AoA spectral points.

Example 7 includes the subject matter of Example 6, and optionally,wherein the radar processor is configured to dynamically set thedetection threshold based on one or more detection criteria.

Example 8 includes the subject matter of any one of Examples 1-7, andoptionally, wherein the radar processor is configured to determine atleast one 1D AoA spectrum of the first 1D AoA spectrum or the second 1DAoA spectrum according to a super resolution spectrum analysisalgorithm.

Example 9 includes the subject matter of Example 8, and optionally,wherein the radar processor is configured to determine a particular 1DAoA spectrum corresponding to a particular dimension of the firstdimension or the second dimension by determining a covariance matrixcorresponding to the particular dimension, and determining theparticular 1D AoA spectrum according to the super resolution spectrumanalysis algorithm based on the covariance matrix corresponding to theparticular dimension, the radar processor is configured to determine thecovariance matrix corresponding to the particular dimension by applyingto the radar Rx data at least one of a Spatial smoothing technique or aForward-Backward technique.

Example 10 includes the subject matter of Example 9, and optionally,wherein the radar processor is configured to determine the covariancematrix corresponding to the particular dimension based on combined Rxdata corresponding to a plurality of 1D antenna indexes in a firstantenna dimension of the 2D radar antenna, wherein combined Rx datacorresponding to a 1D antenna index in the first antenna dimension isbased on a combination of a plurality of data values in the radar Rxdata, which correspond to the 1D antenna index in the first antennadimension, the plurality of data values corresponding to a plurality ofantenna indexes in a second antenna dimension of the 2D radar antenna.

Example 11 includes the subject matter of any one of Examples 8-10, andoptionally, wherein the super resolution spectrum analysis algorithmcomprises a Minimum Variance Distortionless Response (MVDR) algorithm, aMinimum Power Distortionless Response (MPDR) algorithm, or a MultipleSignal Classification (MUSIC) algorithm.

Example 12 includes the subject matter of any one of Examples 1-11, andoptionally, wherein the radar processor is configured to determine atleast one 1D AoA spectrum of the first 1D AoA spectrum or the second 1DAoA spectrum according to a delay-and-sum algorithm.

Example 13 includes the subject matter of any one of Examples 1-12, andoptionally, wherein the radar processor is configured to determine aparticular 1D AoA spectrum corresponding to a particular dimension ofthe first dimension or the second dimension by determining a pluralityof intermediate 1D AoA spectrums corresponding to a respective pluralityof subarrays in the particular dimension of the 2D radar antenna, and todetermine the particular 1D AoA spectrum based on a combination of theplurality of intermediate 1D AoA spectrums.

Example 14 includes the subject matter of Example 13, and optionally,wherein the particular dimension comprises the azimuth dimension, andthe plurality of subarrays comprises a plurality of rows of the 2D radarantenna.

Example 15 includes the subject matter of Example 13, and optionally,wherein the particular dimension comprises the elevation dimension, andthe plurality of subarrays comprises a plurality of columns of the 2Dradar antenna.

Example 16 includes the subject matter of any one of Examples 1-15, andoptionally, wherein the radar processor is configured to determine thefirst 1D AoA spectrum according to a first spectrum analysis algorithm,and to determine the second 1D AoA spectrum according to a secondspectrum analysis algorithm different from the first spectrum analysisalgorithm.

Example 17 includes the subject matter of any one of Examples 1-16, andoptionally, wherein the radar processor is configured to determine thefirst 1D AoA spectrum and the second 1D AoA spectrum according to a samespectrum analysis algorithm.

Example 18 includes the subject matter of any one of Examples 1-17, andoptionally, wherein the radar processor is configured to determine theone or more first object hypotheses according to a first objectdetection scheme, and to determine the one or more second objecthypotheses according to a second object detection scheme different fromthe first object detection scheme.

Example 19 includes the subject matter of any one of Examples 1-17, andoptionally, wherein the radar processor is configured to determine theone or more first object hypotheses and the one or more second objecthypotheses according to a same object detection scheme.

Example 20 includes the subject matter of any one of Examples 1-19, andoptionally, wherein the 2D AoA spectrum analysis comprises a superresolution spectrum analysis algorithm.

Example 21 includes the subject matter of any one of Examples 1-19, andoptionally, wherein the 2D AoA spectrum analysis comprises adelay-and-sum algorithm.

Example 22 includes the subject matter of any one of Examples 1-19, andoptionally, wherein the 2D AoA spectrum analysis comprises amaximum-likelihood (ML) algorithm.

Example 23 includes the subject matter of any one of Examples 1-22, andoptionally, wherein the radar processor is configured to determine thefirst 1D AoA spectrum, the second 1D AoA spectrum, and the 2D AoAinformation according to a coupled coordinate system.

Example 24 includes the subject matter of any one of Examples 1-22, andoptionally, wherein the radar processor is configured to determine thefirst 1D AoA spectrum, the second 1D AoA spectrum, and the 2D AoAinformation according to a non-coupled coordinate system.

Example 25 includes the subject matter of any one of Examples 1-24, andoptionally, wherein the radar processor is configured to determine thesecond 1D AoA spectrum based on the one or more first object hypothesesin the first dimension based on the first 1D AoA spectrum.

Example 26 includes the subject matter of Example 25, and optionally,wherein the first dimension of the Azimuth-Elevation domain comprises anazimuth dimension, and the second dimension of the Azimuth-Elevationdomain comprises an elevation dimension.

Example 27 includes the subject matter of Example 25, and optionally,wherein the first dimension of the Azimuth-Elevation domain comprises anelevation dimension, and the second dimension of the Azimuth-Elevationdomain comprises an azimuth dimension.

Example 28 includes the subject matter of any one of Examples 1-27, andoptionally, wherein the radar processor is configured to determine theplurality of 2D object hypotheses based on a respective plurality ofdifferent combinations of a hypothesis from the first object hypothesesand a hypothesis from the second object hypotheses.

Example 29 includes the subject matter of any one of Examples 1-28, andoptionally, wherein the radar processor is configured to generate the 2DAoA information configured for a Field of View (FOV) parameter and aresolution parameter, and wherein a count of the plurality of 2D objecthypotheses is less than 10% of a count of points in theAzimuth-Elevation domain according to the FOV parameter and theresolution parameter.

Example 30 includes the subject matter of Example 29, and optionally,wherein the count of the plurality of 2D object hypotheses is less than5% of the count of points in the Azimuth-Elevation domain according tothe FOV parameter and the resolution parameter.

Example 31 includes the subject matter of Example 29, and optionally,wherein the count of the plurality of 2D object hypotheses is less than1% of the count of points in the Azimuth-Elevation domain according tothe FOV parameter and the resolution parameter.

Example 32 includes the subject matter of any one of Examples 1-31, andoptionally, wherein the 2D radar antenna comprises a plurality of Rxantennas and a plurality of Transmit (Tx) antennas.

Example 33 includes the subject matter of any one of Examples 1-32, andoptionally, wherein the 2D radar antenna comprises a 2DMultiple-Input-Multiple-Output (MIMO) radar antenna.

Example 34 includes the subject matter of any one of Examples 1-33, andoptionally, comprising the 2D radar antenna, and a plurality of Rxchains to generate the radar Rx data based on radar signals received viaa plurality of Rx antennas of the 2D radar antenna.

Example 35 includes the subject matter of Example 34, and optionally,comprising a vehicle, the vehicle comprising a system controller tocontrol one or more systems of the vehicle based on the radarinformation.

Example 36 includes an apparatus comprising means for executing any ofthe described operations of Examples 1-35.

Example 37 includes a machine-readable medium that stores instructionsfor execution by a processor to perform any of the described operationsof Examples 1-35.

Example 38 includes an apparatus comprising a memory; and processingcircuitry configured to perform any of the described operations ofExamples 1-35.

Example 39 includes a method including any of the described operationsof Examples 1-35.

Functions, operations, components and/or features described herein withreference to one or more aspects, may be combined with, or may beutilized in combination with, one or more other functions, operations,components and/or features described herein with reference to one ormore other aspects, or vice versa.

While certain features have been illustrated and described herein, manymodifications, substitutions, changes, and equivalents may occur tothose skilled in the art. It is, therefore, to be understood that theappended claims are intended to cover all such modifications and changesas fall within the true spirit of the disclosure.

What is claimed is:
 1. An apparatus comprising a radar processor, theradar processor comprising: an input to receive radar receive (Rx) data,the radar Rx data based on radar signals of a Two Dimensional (2D) radarantenna; and a processor configured to: determine a firstone-dimensional (1D) Angle of Arrival (AoA) spectrum corresponding to afirst dimension of an Azimuth-Elevation domain based on the radar Rxdata; determine a second 1D AoA spectrum corresponding to a seconddimension of the Azimuth-Elevation domain based on the radar Rx data;detect one or more first object hypotheses in the first dimension basedon the first 1D AoA spectrum; detect one or more second objecthypotheses in the second dimension based on the second 1D AoA spectrum;determine a plurality of 2D object hypotheses corresponding to theAzimuth-Elevation domain based on the first object hypotheses and thesecond object hypotheses; and generate 2D AoA information in theAzimuth-Elevation domain based on a 2D AoA spectrum analysis of theradar Rx data according to the plurality of 2D object hypotheses.
 2. Theapparatus of claim 1, wherein the radar processor is configured to:determine a candidate set of 2D object hypotheses based on the firstobject hypotheses and the second object hypotheses; determine a reducedcandidate set of 2D object hypotheses by excluding one or more 2D objecthypotheses from the candidate set of 2D object hypotheses based on atleast one exclusion criterion; and generate the 2D AoA information basedon the 2D AoA spectrum analysis of the radar Rx data according to thereduced candidate set of 2D object hypotheses.
 3. The apparatus of claim1, wherein the radar processor is configured to identify one or moreregions of the Azimuth-Elevation domain, and to determine at least one1D AoA spectrum of the first 1D AoA spectrum or the second 1D AoAspectrum in the one or more regions of the Azimuth-Elevation domain. 4.The apparatus of claim 3, wherein the radar processor is configured toidentify the one or more regions based on a coarse AoA spectrum analysisof the Rx radar data, a resolution of the coarse AoA spectrum analysisis lower than a resolution of the at least one 1D AoA spectrum.
 5. Theapparatus of claim 3, wherein the radar processor is configured toidentify the one or more regions based on at least one of mapinformation, scene occlusions, a planned maneuver, a trajectory or aTransmit (TX) Beamforming (BF) configuration.
 6. The apparatus of claim1, wherein the radar processor is configured to determine a plurality ofAoA spectral points based on the 2D AoA spectrum analysis, to identifyone or more selected AoA spectral points from the plurality of AoAspectral points based on a detection threshold, and to generate the 2DAoA information comprising 2D AoA information corresponding to theselected AoA spectral points.
 7. The apparatus of claim 6, wherein theradar processor is configured to dynamically set the detection thresholdbased on one or more detection criteria.
 8. The apparatus of claim 1,wherein the radar processor is configured to determine at least one 1DAoA spectrum of the first 1D AoA spectrum or the second 1D AoA spectrumaccording to a super resolution spectrum analysis algorithm.
 9. Theapparatus of claim 8, wherein the radar processor is configured todetermine a particular 1D AoA spectrum corresponding to a particulardimension of the first dimension or the second dimension by determininga covariance matrix corresponding to the particular dimension, anddetermining the particular 1D AoA spectrum according to the superresolution spectrum analysis algorithm based on the covariance matrixcorresponding to the particular dimension, the radar processor isconfigured to determine the covariance matrix corresponding to theparticular dimension by applying to the radar Rx data at least one of aSpatial smoothing technique or a Forward-Backward technique.
 10. Theapparatus of claim 9, wherein the radar processor is configured todetermine the covariance matrix corresponding to the particulardimension based on combined Rx data corresponding to a plurality of 1Dantenna indexes in a first antenna dimension of the 2D radar antenna,wherein combined Rx data corresponding to a 1D antenna index in thefirst antenna dimension is based on a combination of a plurality of datavalues in the radar Rx data, which correspond to the 1D antenna index inthe first antenna dimension, the plurality of data values correspondingto a plurality of antenna indexes in a second antenna dimension of the2D radar antenna.
 11. The apparatus of claim 8, wherein the superresolution spectrum analysis algorithm comprises a Minimum VarianceDistortionless Response (MVDR) algorithm, a Minimum Power DistortionlessResponse (MPDR) algorithm, or a Multiple Signal Classification (MUSIC)algorithm.
 12. The apparatus of claim 1, wherein the radar processor isconfigured to determine a particular 1D AoA spectrum corresponding to aparticular dimension of the first dimension or the second dimension bydetermining a plurality of intermediate 1D AoA spectrums correspondingto a respective plurality of subarrays in the particular dimension ofthe 2D radar antenna, and to determine the particular 1D AoA spectrumbased on a combination of the plurality of intermediate 1D AoAspectrums.
 13. The apparatus of claim 12, wherein the particulardimension comprises the azimuth dimension, and the plurality ofsubarrays comprises a plurality of rows of the 2D radar antenna.
 14. Theapparatus of claim 12, wherein the particular dimension comprises theelevation dimension, and the plurality of subarrays comprises aplurality of columns of the 2D radar antenna.
 15. The apparatus of claim1, wherein the radar processor is configured to determine the first 1DAoA spectrum according to a first spectrum analysis algorithm, and todetermine the second 1D AoA spectrum according to a second spectrumanalysis algorithm different from the first spectrum analysis algorithm.16. The apparatus of claim 1, wherein the radar processor is configuredto determine the one or more first object hypotheses according to afirst object detection scheme, and to determine the one or more secondobject hypotheses according to a second object detection schemedifferent from the first object detection scheme.
 17. The apparatus ofclaim 1, wherein the radar processor is configured to determine thesecond 1D AoA spectrum based on the one or more first object hypothesesin the first dimension based on the first 1D AoA spectrum.
 18. Theapparatus of claim 1, wherein the radar processor is configured togenerate the 2D AoA information configured for a Field of View (FOV)parameter and a resolution parameter, and wherein a count of theplurality of 2D object hypotheses is less than 10% of a count of pointsin the Azimuth-Elevation domain according to the FOV parameter and theresolution parameter.
 19. The apparatus of claim 1 comprising the 2Dradar antenna, and a plurality of Rx chains to generate the radar Rxdata based on radar signals received via a plurality of Rx antennas ofthe 2D radar antenna.
 20. A product comprising one or more tangiblecomputer-readable non-transitory storage media comprisingcomputer-executable instructions operable to, when executed by at leastone processor, enable the at least one processor to cause a radar deviceto: determine a first one-dimensional (1D) Angle of Arrival (AoA)spectrum corresponding to a first dimension of an Azimuth-Elevationdomain based on radar receive (Rx) data, the radar Rx data based onradar signals of a Two Dimensional (2D) radar antenna; determine asecond 1D AoA spectrum corresponding to a second dimension of theAzimuth-Elevation domain based on the radar Rx data; detect one or morefirst object hypotheses in the first dimension based on the first 1D AoAspectrum; detect one or more second object hypotheses in the seconddimension based on the second 1D AoA spectrum; determine a plurality of2D object hypotheses corresponding to the Azimuth-Elevation domain basedon the first object hypotheses and the second object hypotheses; andgenerate 2D AoA information in the Azimuth-Elevation domain based on a2D AoA spectrum analysis of the radar Rx data according to the pluralityof 2D object hypotheses.
 21. The product of claim 20, wherein theinstructions, when executed, cause the radar device to determine acandidate set of 2D object hypotheses based on the first objecthypotheses and the second object hypotheses, to determine a reducedcandidate set of 2D object hypotheses by excluding one or more 2D objecthypotheses from the candidate set of 2D object hypotheses based on atleast one exclusion criterion, and to generate the 2D AoA informationbased on the 2D AoA spectrum analysis of the radar Rx data according tothe reduced candidate set of 2D object hypotheses.
 22. The product ofclaim 20, wherein the instructions, when executed, cause the radardevice to identify one or more regions of the Azimuth-Elevation domain,and to determine at least one 1D AoA spectrum of the first 1D AoAspectrum or the second 1D AoA spectrum in the one or more regions of theAzimuth-Elevation domain.
 23. The product of claim 20, wherein theinstructions, when executed, cause the radar device to determine aplurality of AoA spectral points based on the 2D AoA spectrum analysis,to identify one or more selected AoA spectral points from the pluralityof AoA spectral points based on a detection threshold, and to generatethe 2D AoA information comprising 2D AoA information corresponding tothe selected AoA spectral points.
 24. A vehicle comprising: a systemcontroller configured to control one or more vehicular systems of thevehicle based on radar information; and a radar device configured toprovide the radar information to the system controller, the radar devicecomprising: a Two Dimensional (2D) radar antenna comprising a pluralityof Transmit (Tx) antennas to transmit Tx radar signals, and a pluralityof Receive (Rx) antennas to receive Rx radar signals based on the Txradar signals; and a processor configured to generate the radarinformation based on radar Rx data, the radar Rx data based on the Rxradar signals, the radar information comprising 2D Angle of Arrival(AoA) information in an Azimuth-Elevation domain, wherein the processoris configured to determine a first one-dimensional (1D) AoA spectrumcorresponding to a first dimension of the Azimuth-Elevation domain basedon the radar Rx data, to determine a second 1D AoA spectrumcorresponding to a second dimension of the Azimuth-Elevation domainbased on the radar Rx data, to detect one or more first objecthypotheses in the first dimension based on the first 1D AoA spectrum, todetect one or more second object hypotheses in the second dimensionbased on the second 1D AoA spectrum, to determine a plurality of 2Dobject hypotheses corresponding to the Azimuth-Elevation domain based onthe first object hypotheses and the second object hypotheses, and togenerate the 2D AoA information based on a 2D AoA spectrum analysis ofthe radar Rx data according to the plurality of 2D object hypotheses.25. The vehicle of claim 24, wherein the processor is configured todetermine a candidate set of 2D object hypotheses based on the firstobject hypotheses and the second object hypotheses, to determine areduced candidate set of 2D object hypotheses by excluding one or more2D object hypotheses from the candidate set of 2D object hypothesesbased on at least one exclusion criterion, and to generate the 2D AoAinformation based on the 2D AoA spectrum analysis of the radar Rx dataaccording to the reduced candidate set of 2D object hypotheses.